Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Many educational institutions in North America have declared a commitment to enhancing the diversity of their students, and to providing a learning environment free of discrimination. This diversity unequivocally includes sexual orientation as well as gender identity and expression, and we teachers are expected to play a role in fulfilling this commitment. Nevertheless, there are few opportunities for us to learn about the diversity of gender and sexuality in pre-service training or in-service professional development for Japanese-language education. This paper addresses issues that may create challenges for LGBTQ learners of Japanese, paying special attention to heteronormativity in Japanese language teaching materials and linguistic norms and ideology regarding gendered expression in Japanese, and suggests ways teachers might deal with these issues in order to create an inclusive learning environment for all students regardless of their gender and sexuality.
(NAPS; Marchewka et al., 2014). We collected ratings of perceived valence and arousal for both material groups and recorded eye movements of 42 participants during reading and picture viewing. Linear mixed-effects models were performed to analyze effects of valence (i.e., valence category, valence rating) and stimulus domain (i.e., textual, pictorial) on ratings of perceived valence and arousal, eye movements in reading, and eye movements in picture viewing. Results supported the success of our experimental manipulation: emotionally positive stimuli (i.e., vignettes, pictures) were perceived more positively and less arousing than emotionally negative ones. The cross-domain comparison indicated that vignettes are able to induce stronger valence effects than their pictorial counterparts, no differences between vignettes and pictures regarding effects on perceived arousal were found. Analyses of eye movements in reading replicated results from experiments using isolated words and sentences: perceived positive text valence attracted shorter reading times than perceived negative valence at both the supralexical and lexical level. In line with previous findings, no emotion effects on eye movements in picture viewing were found. This is the first eye tracking study reporting superior valence effects for vignettes compared to pictures and valence-specific effects on eye movements in reading at the supralexical level.
Emotion research typically searches for consistency and specificity in physiological activity across instances of an emotion category, such as anger or fear, yet studies to date have observed more variation than expected. In the present study, we adopt an alternative approach, searching inductively for structure within variation, both within and across participants. Following a novel, physiologically-triggered experience sampling procedure, participants’ self-reports and peripheral physiological activity were recorded when substantial changes in cardiac activity occurred in the absence of movement. Unsupervised clustering analyses revealed variability in the number and nature of patterns of physiological activity that recurred within individuals, as well as in the affect ratings and emotion labels associated with each pattern. There were also broad patterns that recurred across individuals. These findings support a constructionist account of emotion which, drawing on Darwin, proposes that emotion categories are populations of variable instances tied to situation-specific needs.
Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29\\% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).
We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-theart while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.
The importance of affect processing to human behavior has long driven researchers to pursue its measurement. In this study, we compared the relative fidelity of measurements of neural activation and physiology (i.e., heart rate change) in detecting affective valence induction across a broad continuum of conveyed affective valence. We combined intra-subject neural activation based multivariate predictions of affective valence with measures of heart rate (HR) deceleration to predict predefined normative affect rating scores for stimuli drawn from the International Affective Picture System (IAPS) in a population (n = 50) of healthy adults. In sum, we found that patterns of neural activation and HR deceleration significantly, and uniquely, explain the variance in normative valent scores associated with IAPS stimuli; however, we also found that patterns of neural activation explain a significantly greater proportion of that variance. These traits persisted across a range of stimulus sets, differing by the polar-extremity of their positively and negatively valent subsets, which represent the positively and negatively valent polar-extremity of stimulus sets reported in the literature. Overall, these findings support the acquisition of heart rate deceleration concurrently with fMRI to provide convergent validation of induced affect processing in the dimension of affective valence.
For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%
This paper discusses the theoretical bases as well as the pragmatic implementation of the lemmatization of the Late Latin Charter Treebanks (LLCT). LLCT is a set of three dependency treebanks (LLCT1, LLCT2, LLCT3) of Early Medieval Latin documentary texts (charters) written in Italy between AD 714 and 1000 (c. 594,000 tokens). The original model for the lemmatization of LLCT was the Latin Dependency Treebank (LDT), which is mainly Classical standard Latin and based on the entries of Lewis and Short’s Latin Dictionary. Since LLCT reflects later linguistic developments of Latin and contains a plethora of non-standard proper names, particular attention is paid to how non-standard lexemes are lemmatized systematically to make the lemmatization maximally usable. The theoretical underpinnings to manage the lemmatization boil down to two principles: the evolutionary principle and the parsimony principle.
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word-order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.
Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift-reduce transition system, which does not align with how humans are known to parse sentences. Psycholinguistic research suggests that human parsing is strongly incremental: humans grow a single parse tree by adding exactly one token at each step. In this paper, we propose a novel transition system called attach-juxtapose. It is strongly incremental; it represents a partial sentence using a single tree; each action adds exactly one token into the partial tree. Based on our transition system, we develop a strongly incremental parser. At each step, it encodes the partial tree using a graph neural network and predicts an action. We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. Code is available at this https URL.
We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG). TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sisteradjunction and tree-wrapping substitution. TWGs can be extracted from constituency treebanks and aim at representing long distance dependencies (LDDs) in a linguistically adequate way. We present a parsing algorithm for TWGs based on neural supertagging and A * parsing. We extract a TWG for English from the treebanks for Role and Reference Grammar and discuss first parsing results with this grammar.
In natural vision, noisy and distorted visual inputs often change our perceptual strategy in scene perception. However, it is unclear the extent to which the affective meaning embedded in the degraded natural scenes modulates our scene understanding and associated eye movements. In this eye-tracking experiment by presenting natural scene images with different categories and levels of emotional valence (high-positive, medium-positive, neutral/low-positive, medium-negative, and high-negative), we systematically investigated human participants' perceptual sensitivity (image valence categorization and arousal rating) and image-viewing gaze behaviour to the changes of image resolution. Our analysis revealed that reducing image resolution led to decreased valence recognition and arousal rating, decreased number of fixations in image-viewing but increased individual fixation duration, and stronger central fixation bias. Furthermore, these distortion effects were modulated by the scene valence with less deterioration impact on the valence categorization of negatively valenced scenes and on the gaze behaviour in viewing of high emotionally charged (high-positive and high-negative) scenes. It seems that our visual system shows a valence-modulated susceptibility to the image distortions in scene perception.
This article presents a theory of the initiation and incrementation mechanisms whereby individual phonetic innovations become community-wide sound changes. The theory asserts that language learners are community-oriented and momentum-sensitive: they are community-oriented in that they acquire and obey a mental representation of the collective linguistic norm of their speech community, rejecting individual idiosyncrasies; they are momentum-sensitive in that their mental representation of the community norm includes an age vector encoding linguistic differences between age groups. The theory is shown to fulfil four critical desiderata: (i) it accounts for the sporadic and localized occurrence of community-wide sound change, (ii) it incorporates Ohala’s prediction of a lawful relationship between the strength of the phonetic biases driving individual innovation and the typological frequency of the corresponding sound changes, (iii) it explains how community-wide sound change advances by intergenerational incrementation producing adolescent peaks in apparent time, and (iv) it reliably generates monotonic—including sigmoid—diachronic trajectories. Moreover, the hypotheses of community orientation and sensitivity to momentum, combined with the mechanical effects of density of contact, suffice to explain several macroscopic phenomena in the propagation of sound change, including class stratification, the curvilinear pattern in change from below, and the existence of change reversals. During propagation, linguistic variants do acquire indexical value, and so social meaning, but this produces only small-scale attitudinal effects; it is not the force that drives the intergenerational incrementation of sound change.
UDon2 is an open-source library for manipulating dependency trees represented in the CoNLL-U format. The library is compatible with the Universal Dependencies. UDon2 is aimed at developers of downstream Natural Language Processing applications that require manipulating dependency trees on the sentence level (in addition to other available tools geared towards working with treebanks).
One of the biggest attractions in the tourism industry in Bandung is nature tourism. There is still such a constraint related to get information about nature tourism in Bandung because new attractions in Bandung always appear every year. This is felt particularly for foreign tourists outside of Bandung. Tourists are still confused to find new and popular tourist attractions, which are places that are worth visiting or not. By implementing Cyber-Physical-Social System (CPSS) with a new approach that is emphasized on social aspect in smart tourism based on Service Oriented Architecture (SOA) as methodology can influence other travelers to visit tourist attractions in Bandung. The main results are tourists will get information such as location, route, images, rating, captions of tourist attractions, and the most important thing is to be able to exchange information with others. Smart tourism is more flexible because it is web based and does not depend on the operating system used, does not require database storage, does not take up storage space, and is free. Tourists can access smart tourism anytime and anywhere.
This study examined the syntactic impairments of Chinese Alzheimer’s disease patients with a dependency network approach. The dependency treebanks and dependency networks are constructed from the discourses of both the patient group and its healthy peers. By analysing the contrasts in the dependency networks of the two groups, we found that 1) the mean dependency distance (MDD) of the AD group is shorter than that of the HP group; furthermore, the MDDs of both AD and HP groups are far below the standard Chinese MDD; 2) the content words like remember, forget, know, etc. and the negative forms of the verbs like don’t know, can’t remember, can’t say, etc. show highly repetitive uncertain and negative expressions that are typical of the predicates of the clauses of AD patients; 3) the function word vertices in the AD dependency network have distinctive network parameters such as higher ‘betweenness’ centrality, closeness centrality, and clustering coefficients, etc., indicating that the syntax of AD is impaired and features more simplified stereotypes. These results indicate that the syntax of the AD group has been impaired from parts of speech to the whole syntactic structure.
BACKGROUND: Interest in the measurement of the temporal dynamics of people's emotional lives has risen substantially in psychological and medical research. Emotions fluctuate and change over time, and measuring the ebb and flow of people's affective experiences promises enhanced insights into people's health and functioning. Researchers have used a variety of intensive longitudinal assessment (ILA) methods to create measures of emotion dynamics, including ecological momentary assessments (EMAs), end-of-day (EOD) diaries, and the day reconstruction method (DRM). To date, it is unclear whether they can be used interchangeably or whether ostensibly similar emotion dynamics captured by the methods differ in meaningful ways. OBJECTIVE: This study aims to examine the extent to which different ILA methods yield comparable measures of intraindividual emotion dynamics. METHODS: Data from 90 participants aged 50 years or older were collected in a probability-based internet panel, the Understanding America Study, and analyzed. Participants provided positive and negative affect ratings using 3 ILA methods: (1) smartphone-based EMA, administered 6 times per day over 1 week, (2) web-based EOD diaries, administered daily over the same week, and (3) web-based DRM, administered once during that week. We calculated 11 measures of emotion dynamics (addressing mean levels, variability, instability, and inertia separately for positive and negative affect, as well as emotion network density, mixed emotions, and emotional dialecticism) from each ILA method. The analyses examined mean differences and correlations of scores addressing the same emotion dynamic across the ILA methods. We also compared the patterns of intercorrelations among the emotion dynamics and their relationships with health outcomes (general health, pain, and fatigue) across ILA methods. RESULTS: Emotion dynamics derived from EMAs and EOD diaries demonstrated moderate-to-high correspondence for measures of mean emotion levels (ρ≥0.95), variability (ρ≥0.68), instability (ρ≥0.51), mixed emotions (ρ=0.92), and emotional dialecticism (ρ=0.57), and low correspondence for measures of inertia (ρ≥0.17) and emotion network density (ρ=0.36). DRM-derived measures showed correlations with EMAs and EOD diaries that were high for mean emotion levels and mixed emotions (ρ≥0.74), moderate for variability (ρ=0.38-.054), and low to moderate for other measures (ρ=0.03-0.41). Intercorrelations among the emotion dynamics showed high convergence across EMAs and EOD diaries, and moderate convergence between the DRM and EMAs as well as EOD diaries. Emotion dynamics from all 3 ILA methods produced very similar patterns of relationships with health outcomes. CONCLUSIONS: EMAs and EOD diaries provide corresponding information about individual differences in various emotion dynamics, whereas the DRM provides corresponding information about emotion levels and (to a lesser extent) variability, but not about more complex emotion dynamics. Our results caution researchers against viewing these ILA methods as universally interchangeable.
Word embeddings have been extensively used for sentiment analysis tasks. However, typical existing algorithms only model the syntactic context of words but fail to capture sufficient sentiment information of text, which affects the performance of sentiment analysis. Therefore, this paper presents an word vector refinement model based on improved genetic algorithm, which uses sentiment lexicon to obtain the sentiment ranking of the semantic nearest neighbors of the target word, and uses an improved genetic algorithm to optimize the vector representations of words such that they get the sentiment information of the word. Experimental results show that the proposed model can improve conventional word embeddings for binary classification on Internet Movie Database (IMDB) and fine-grained classification on Stanford Sentiment Treebank (SST).
Extinction of appetitive conditioning is regarded as an important model for the treatment of psychiatric disorders like addiction. However, very few studies have investigated its neural correlates. Therefore, we investigated neural correlates of appetitive extinction in a large human sample including all genders (N = 76, 40 females) to replicate and extend results from a previous study. During differential appetitive conditioning, one stimulus (CS+) was paired with the chance to win a monetary reward, whereas another stimulus (CS-) was not. During appetitive extinction on the next day, neither the CS+ nor the CS- were reinforced. After successful acquisition of appetitive conditioning, the extinction phase elicited significant reductions of valence and arousal ratings toward the CS+ and a significant reduction in skin conductance responses to the CS+ from early to late extinction. On a neural level, early extinction showed significant differential (CS+ - CS-) activation in dACC and hippocampus, whereas involvement of the vACC and caudate nucleus did not replicate. The differential activation of amygdala and nucleus accumbens during late extinction was replicated, with the amygdala displaying significantly higher differential activation during the late phase of extinction as compared to the early phase of extinction. We show discernible signals for reward learning and extinction in subregions of amygdala and nucleus accumbens after extinction learning. This successful replication underlines the role of nucleus accumbens and amygdala in neural models of appetitive extinction in humans that was previously only based on animal findings.
In healthy individuals, stimuli associated with injury (such as those depicting blood or wounds) tend to evoke negative responses on both self-report and psychophysiological measures. Such an instinctive aversion makes sense from an evolutionary perspective. However, to engage in nonsuicidal self-injury (NSSI), this natural barrier must be overcome. The Benefits and Barriers model of NSSI predicts that people who engage in NSSI will show diminished aversion to NSSI-related stimuli compared to controls who do not engage in NSSI. We tested this hypothesis in a pilot study assessing 30 adults, 15 of whom reported current skin cutting and 15 of whom had no history of NSSI. Functional magnetic resonance imaging (fMRI) data were collected while participants viewed neutral, positive, and negative images selected from the International Affective Picture System. Participants also viewed NSSI images depicting razors, scalpels, or wounds caused by cutting. Compared to healthy control (HC) participants, the NSSI group showed decreased amygdala and increased cingulate cortex (CC) and orbitofrontal cortex (OFC) activation to NSSI and negative images. They also showed increased amygdalar and OFC activation to positive images. Neither the control group nor the NSSI group demonstrated significant activation within regions more typically associated with reward during any of the conditions; however, positive and negative affect ratings collected throughout the course of the task suggested that none of the affective conditions were viewed as rewarding. Although preliminary, these findings are suggestive of reduced limbic and greater cortical processing of NSSI stimuli in those with a history of this behavior. This has potentially important implications for current models of NSSI as well as for its treatment.
The study aims to shed light on the modern forms of linguistic dictionaries and show the influence of the Internet at the present time on them. These lexical changes that the Internet has caused since the late nineties must be understood primarily as a form of additional development of the dictionary format, which meets the needs and requirements of users and tries to adapt them to the principles of dictionaries, which in turn led to the emergence of a modern and very sophisticated dictionary of the Internet. This lexical product is often called the online dictionary, electronic dictionary, online database or lexical database. The results of the numerous survey conducted by the German Language Institute (IDS) in Mannheim show that searching for an Internet dictionary is much easier, more useful, and more exciting. It is interesting that users can make a significant contribution to entering and modifying existing words in the electronic dictionary and influencing the presence of vocabulary online through their desires or ideas or even through their comments. The search for traditional dictionaries is often replaced by electronic dictionaries and lexical databases. Therefore, the introduction of electronic dictionaries and the use of lexical databases in an educational curriculum is imperative and necessary, as it must be emphasized that user interaction is one of the important methodological foundations for the development of electronic تهدف الدراسة إلي تسليط الضوء علي الأشکال الحديثة للمعاجم اللغوية وإظهار تأثير الإنترنت في الوقت الحاضر عليها، حيث تُبرز الدراسة التأثيرات الکبيرة والعميقة للإنترنت علي علم المعاجم العريق. إن هذه المتغييرات المعجمية التي تسبب فيها الإنترنت منذ أواخر التسعينيات ، يجب أن تُفهم في المقام الأول على أنها شکل من أشکال التطوير الإضافي لشکل القاموس، يلبي احتياجات المستخدمين ومتطلباتهم ويحاول تطويعها لمباديء علم المعاجم، الأمر الذي أدى بدوره إلى ظهور معجم الإنترنت الحديث والمتطور للغاية. وغالبًا ما يطلق على هذا المنتج المعجمي معجم الإنترنت أو القاموس الإلکتروني أو قاعدة البيانات على الإنترنت أو قاعدة البيانات المعجمية. وتُظهر نتائج المسح العديدة التي أجراها معهد اللغة الألمانية في مدينة مانهايم أن البحث عن قاموس إنترنت أسهل بکثير وأکثر إفادة وأکثر إثارة. ومن المثير للاهتمام أن المستخدمين يمکنهم أن يسهموا مساهمة کبيرة في إدخال وتعديل الکلمات الحالية في المعجم الإلکتروني والتأثير في وجود المفردات عبر الإنترنت من خلال رغباتهم أو أفکارهم أو حتى من خلال تعليقاتهم. ومما لا شک فيه أنه يمکن إثبات وجود اختلافات کبيرة بين المعجم التقليدي والمعجم الرقمي أو المعجم العادي بمساعدة الکمبيوتر، لأن المعلومات في المعجم التقليدي تعتمد أساساً علي البحث الأبجدي عن المفردات، أما المعجم الإلکتروني فغالبا ما يتعلق باستراتيجيات البحث المختلفه التي يوضحها البحث. ومن المهم في هذا السياق التأکيد على أن القواميس والقواعد هي عنصر مهم في تعلم اللغات الأجنبية ، بغض النظر عن العمر ومستوى اللغة. ومع ذلک ، فقد لوحظ لبعض الوقت أن الأساليب التقليدية لتدريس قواعد اللغة والقواميس التقليدية لم تعد تتکيف مع دروس تعلم اللغات. وغالبا ما يتم استبدال البحث في القواميس التقليدية بالقواميس الإلکترونية وقواعد البيانات المعجمية. لذلک، أصبح إدخال القواميس الإلکترونية واستخدام قواعد البيانات المعجمية في منهج تعليمي أمرًا حتمياً وضروريًا، حيث أنه يجب التأکيد على أن تفاعل المستخدم هي واحدة من الأسس المنهجية الهامة لتطوير المعاجم الإلکترونية.
PURPOSE A culturally appropriate, patient-centered measure of the quality of dying and death is needed to advance palliative care in Africa. We therefore evaluated the Quality of Dying and Death Questionnaire (QODD) in a Kenyan hospice sample and compared item ratings with those from a Canadian advanced-cancer sample. METHODS Caregivers of deceased patients from three Kenyan hospices completed the QODD. Their QODD item ratings were compared with those from 602 caregivers of deceased patients with advanced cancer in Ontario, Canada, and were correlated with overall quality of dying and death ratings. RESULTS Compared with the Ontario sample, outcomes in the Kenyan sample (N = 127; mean age, 48.21 years; standard deviation, 13.57 years) were worse on 14 QODD concerns and on overall quality of dying and death ( P values ≤.001) but better on five concerns, including interpersonal and religious/spiritual concerns ( P values ≤.005). Overall quality of dying was associated with better patient experiences with Symptoms and Personal Care, interpersonal, and religious/spiritual concerns ( P values <.01). Preparation for Death, Treatment Preferences, and Moment of Death items showed the most omitted ratings. CONCLUSION The quality of dying and death in Kenya is worse than in a setting with greater PC access, except in interpersonal and religious/spiritual domains. Cultural differences in perceptions of a good death and the acceptability of death-related discussions may affect ratings on the QODD. This measure requires revision and validation for use in African settings, but evidence from such patient-centered assessment tools can advance palliative care in this region.
Treebank is one of the important and useful resources in natural language processing represented in two different annotated schemas: phrase and dependency structures. There are many works that convert a phrase structure into a dependency structure and vice versa. Most of them are based that exploit the handcrafted head percolation table and argument table in predefined deterministic ways. In this article, we propose a method to convert a dependency structure into a phrase structure by enriching a trainable model of former hybrid strategy approach. By adding a classifier to the algorithm and using postprocessing modification, the quality of conversion is increased. We evaluate our method in two different languages, English and Persian, and then analyze the errors. The results of our experiments show a 46.01% reduction of error rate in English and 76.50% for Persian compared to our baseline. We build a new phrase structure treebank by converting 10,000 sentences of Persian dependency treebank into corresponding phrase structures and correcting them manually.
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacking or re-ranking of partially-built parse trees to build a complete parse tree and are stuck with slower training for the necessity of dynamic oracle training. The latter, graph-based models, may boast better performance but are unfortunately marred by polynomial time inference. In this paper, we propose a novel parsing order objective, resulting in a novel dependency parsing model capable of both global (in sentence scope) feature extraction as in graph models and linear time inference as in transitional models. The proposed global greedy parser only uses two arc-building actions, left and right arcs, for projective parsing. When equipped with two extra non-projective arc-building actions, the proposed parser may also smoothly support non-projective parsing. Using multiple benchmark treebanks, including the Penn Treebank (PTB), the CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our parser and demonstrate that the proposed novel parser achieves good performance with faster training and decoding.
This chapter highlights the depth of the relationship between language and emotions. It aims to clarify some important distinctions and terms with respect to the linguistic encoding of emotions. Anthropological research has long confirmed that people in different human groups deal with emotional experience in different ways and different languages also offer very different means to talk about it. The chapter shows that emotional experience involves both some physiological and neurological mechanisms, which – for some of them – may be universal, as well as the cognitive organization of these mechanisms into experience, governed by culturally specific social and linguistic norms. A basic criterion that differentiates between descriptive and expressive linguistic resources is the semiotic status of the linguistic devices in question. Descriptive resources consist mostly of lexical resources, that is words, and some constructions.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-2988). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
People who experience trauma can develop enduring trauma-related symptoms. In daily life, post-trauma symptoms (e.g., elevated physiological arousal) can be triggered by affectively salient cues in the environment, especially by cues that act as trauma reminders. Trauma exposure is associated with enduring changes in two biological stress systems: the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. In women, activity in both systems is additionally modulated by fluctuations in levels of sex hormones (e.g., estradiol), which could influence physiological responses to trauma reminders. Additionally, previous work has linked the sex hormone estradiol with affect, suggesting that menstrual cycle might influence trauma-related symptoms or daily affect more broadly within the context of trauma exposure. However, we do not yet have a clear understanding of how estradiol influences affective experiences post-trauma. We used a multi-method approach to examine the influence of estradiol on daily affective experiences in a non-clinical, trauma-exposed sample of 40 naturally cycling premenopausal women. The first specific goal of this study was to test the hypothesis that low estradiol would be related to trauma symptoms, including an asymmetrical profile of SNS and HPA axis stress reactivity to a naturalistic trauma reminder. Lower estradiol was related to greater number and severity of PTSD symptoms, and participants in low versus high estradiol menstrual cycle phases showed higher SNS and reduced HPA axis reactivity to a trauma reminder. These results suggest that lower estradiol is associated with a less adaptive profile of stress system reactivity and increased PTSD symptom expression. The second specific goal of this study was to test the influence of menstrual cycle phase on daily affect in a subset of 30 participants. We assessed affective experience over the course of a 10-day ecological momentary assessment (EMA) period, which included the early follicular (low estradiol) and late follicular (high estradiol) phases. We selected these menstrual cycle phases to capture a portion of the cycle where estradiol increased, whereas progesterone remained low, allowing us to test the effects of estradiol without the confound of progesterone. Participants reported more frequent aversive affective experiences, defined as negatively valenced, high arousal states, including PTSD symptoms, during the early versus late follicular phase. During the early versus late follicular phase, participants also reported greater negative and positive affect and showed greater variability in affective ratings. These results suggest that lower estradiol menstrual cycle phases are characterized by more frequent aversive affective experiences, greater affective lability and increased PTSD symptom severity. Together, these results have potential implications for clinical assessment, as menstrual cycle phase at the time of assessment could influence diagnosis of PTSD or symptom severity. Additionally, clinicians working with women with PTSD might anticipate greater affective lability and increased symptom severity during low estradiol phases of the menstrual cycle.
Over the last few decades, corpora with comprehensive syntactic annotation, known as treebanks or parsed corpora, have been created in various formats for major languages of the world (e.g., As modes of accessing annotation have become more linguistically sophisticated, so these corpus resources have become more relevant for linguistics in general by providing sources of insight into factors that only become visible through analysis generalized over structures: phenomena in co-occurrence, frequency, constituency, embeddability, scope, agreement, dependency, etc. These insights are spurring new research and refinements in both corpus techniques and theoretical understanding. While much research has concentrated on challenges inherent in the creation as well as correction of annotated corpora (e.g., Examples include linking corpora to external resources like lexical databases, abstracting the contents sufficiently to be of use to non-experts, exploration of crosslinguistic patterns, etc. This special issue consists of five articles focused on applying parsed corpora research in three areas: (I) enrichment and
Modern health worries (MHW) represent individual differences in the perceived threat posed to health and well-being by aspects of modern life. Current evidence suggests that MHW are positively associated with trait negative emotionality, and given that trait negative emotionality is associated with state emotional reactivity to environmental stressors, it is reasonable to expect that persons with elevated MHW would show increased state emotional reactivity to MHW-related stimuli. Consequently, this study aimed to investigate the association of MHW with state emotional reactivity (i.e., valence and arousal) to MHW-related stimuli (i.e., images of air pollution). Combining these stimuli with other stimuli varying in valence and arousal allowed us to examine whether MHW are specifically associated with emotional reactivity induced by MHW-related stimuli. A total of 73 college students viewed 48 images encompassing eight different content areas, including a subset of MHW-related images (air pollution); each image was rated for valence and arousal. Participants also completed measures of MHW, trait negative emotionality (i.e., neuroticism), and demographics. After controlling for neuroticism and gender, results suggest that MHW only predicted valence rating for images of air pollution; conversely, MHW appears to be associated with arousal ratings in response to a variety of stimuli. Implications and limitations are discussed.
This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019\ntask 2 of Morphological Analysis and Lemmatization in Context. This task\nrequires us to produce the lemma and morpho-syntactic description of each token\nin a sequence, for 107 treebanks. We approach this task with a hierarchical\nneural conditional random field (CRF) model which predicts each coarse-grained\nfeature (eg. POS, Case, etc.) independently. However, most treebanks are\nunder-resourced, thus making it challenging to train deep neural models for\nthem. Hence, we propose a multi-lingual transfer training regime where we\ntransfer from multiple related languages that share similar typology.\n
BACKGROUND: People experiencing mental illness require services that provide them with a sense of personal safety, a place where they can experience a reduction to their distress and assistance in managing their feelings. Interventions need to explore therapies that enhance feelings of personal safety and comfort for consumers and within a forensic mental health service, therapies and support that can assist in combating the antecedents to violent offending. The practice of Qigong is reported to have numerous health benefits; however, little has been reported regarding the possible benefits of Qigong for people experiencing severe mental illness and, more specifically, for people experiencing severe mental illness who have serious offending histories such as forensic consumers. This study explores the possibility of using Qigong to reduce personal frustrations that can lead to violence. OBJECTIVES: The object of this study was to explore whether Qigong is an effective intervention on positive affect traits for forensic mental health consumers, and whether other benefits are experienced. METHODS: An exploratory design using quantitative and qualitative approaches was used. Consumers participated in weekly Qigong groups delivered for a 10-wk period. Data were collected using an adapted version of the positive affect rating scale measuring the degree to which people experience different positive emotions. Qualitative measures were added to the scale to obtain a deeper understanding of the consumer experience, with 67 scales completed. CONCLUSIONS: Consumers in a forensic hospital responded positively to participating in Qigong groups. Strategies such as Qigong are interventions that mental health clinicians can use to promote positive feelings of personal relaxation, peacefulness, and safety. Qigong can promote positive affective traits for consumers in forensic hospitals. These positive affective traits can act as protective factors to inpatient aggression and violence. Forensic consumers report that Qigong is easy to learn and helpful for them in managing their frustrations. The findings from this study may add to the paucity of data discussing the use of Qigong with consumers as an effective relaxation intervention and possibly as an intervention in reducing negative affective states by promoting positive affective states, thereby reducing aggression and possible violence occurring within the forensic inpatient environment.
Lectometric approaches measure distances between language varieties (dialects, sociolects, registers etc.) by aggregating over observed differences in the realizations of a set of linguistic variables. In <em>lexical</em> lectometry, a variable consists of the alternative lexical expressions for one concept. In <em>corpus-based</em> lectometry, the observed realizations are culled from stratified corpora. Measuring semantically defined variables in corpora, and aggregating over them, poses specific methodological challenges that have been tackled in a number of studies (Heylen & Ruette 2013; Ruette et al. 2014; Ruette, Ehret & Szmrecsanyi 2016) with different statistical techniques, including Distributional Semantic Models. Yet so far, no general framework for corpus-based lexical lectometry has been formulated that systematically describes the issues and options in each step of the procedure so that it can be straightforwardly applied to new data and new languages, other than English (Ruette, Ehret & Szmrecsanyi 2016), Dutch (Geeraerts, Grondelaers & Speelman 1999) and Portuguese (Soares da Silva 2010). This paper can be characterized as a twofold extension of the previous studies. First, it aims to establish a general framework for lexical lectometry research that considers most if not all options for different steps. Second, we want to go beyond the Indo-European languages by extending the framework on a typologically unrelated language, i.e. Chinese varieties. For the general framework, we propose that a proper lexical lectometry research normally should involve the following steps: (1) compilation of a lectally stratified corpus; (2) sampling concepts as measuring points for lectometry; (3) identification of lexical expressions per concept; (4) disambiguation of lexical expressions in corpus data; (5) calculation of aggregated lexico-lectometric distances; (6) evaluation of measurement reliability and validity. For each step, we further provide possible options and caveats. For instance, step 2 and 3 can rely on existing concept-based lexical databases, like a synonym dictionary, or use corpus-driven keyword extraction and semantic vector space models. Step 4 can either make use of token-level distributional semantics models or rely on simpler n-gram language models. To assess the portability of the general framework, both in practical and linguistic-typological terms, we perform a lexical lectometric analysis for varieties of Chinese based on data from large-scale corpora of Mainland Chinese, Taiwan Chinese and Singapore Chinese.
Introduction. At present, there is no doubt that during its historical development, different communities interact with one another or another, which necessarily implies parallelism of language contacts. Modern globalization processes require individuals to adapt to a rapidly changing socio-cultural environment, which implies the ability to interpret cultural phenomena in different linguistic and cultural contexts. Particular attention should be paid to the analysis of the communicative competence of the "interpreter", whose view on culture, its phenomena and forms of being determines the depth of understanding of historical realities and current practices. The aim is to analyze the phenomenon of semi-language as a negative consequence of cultural interference. The tasks are: 1) to characterize the situation of semi-language as an inconsistency or the imposition of one code of one language system on another in terms of intercultural communication; 2) to investigate the peculiarities of the loss of linguistic normality at the macro- and micro- levels. Research methods used in the article, reflect its interdisciplinary character. Research methods include proper linguistic and philosophical methods of analysis. The system-cultural, typological approaches are used in the research. Research results: analysis of the linguistic situation in modern society suggests that no bilingual bearer can have absolutely equal experience of contact with different linguistic groups, because, using certain words, he cannot control the social context. Semi-culture, as we understand it here, is a conflict situation in which the individual finds himself as a result of breaking the tradition. In favorable circumstances, a "semi-cultural" situation is temporal, and in unfavorable circumstances, it becomes a permanent factor, which can lead an individual to become a semi-linguistic one. Semi-language is the inability of an individual who has grown up in a bilingual environment to express his or her thoughts clearly in any language, to adhere to linguistic norms. Insufficient language development is not the only one that threatens the individual in the context of forced bilingualism. The specificity of voluntare (based on contract, consensus, dialogue) communication implies a reflexive interconnection of its subjects. Discussion. The lack of linguistic communication competence of native speakers undermines the rational form of organization of public space, which presupposes the achievement of public consensus. The extent to which the peoples are able to cope with this task is a serious question. Conclusion. Analysis of intercultural communication suggests that differences in culture and language of social groups in contact give rise to the phenomenon of semi-culture as a state of forced existence of personality in two cultures. The functioning of a semi-culture generates a semi-language with the rejection of stylistic, etiquette, etc. norms of differentiation of codes of different language systems. Micro-level code mixing manifests itself in the loss of the linguistic-communicative register, which is easily accessible to the developed "linguistic personality". Macro-level integration processes under half-life conditions lead to the formation of a reduced rather than a single and enriched culture. Semi-formalism in the formal presence of democratic institutions counteracts dialogue practices as the most cooperative form of communicative interaction based on reflective role-sharing.
Introduction: Hoarding behaviour is a common symptom seen in patients with Obsessive Compulsive Disorder (OCD). The phenomenology and prevalence of hoarding in OCD have been under studied in India and the phenomenon is less explored on routine clinical examination. Aim: To study the prevalence and phenomenology of hoarding as a symptom in patients with OCD and tried to elucidate some differences between OCD patients with and without hoarding symptoms. Materials and Methods: A total of 50 patients with OCD and 50 relatives of psychiatric patients were the subjects for the study. The OCD group was administered the Yale Brown Obsessive Compulsive Scale (YBOCS), the Hoarding Rating Scale and the Clutter Image Rating Scale. The 50 cases of OCD were further divided on the presence and absence of hoarding as a symptom into 2 groups and the scores on the scales used were statistically analysed using descriptive statistics like frequency and percentages, chi-square test and unpaired t-test. Results: The mean duration of illness was 8.01±5.17 years and the mean age of onset of the illness was 27.28±7.11 years for all patients with OCD. OCD patients with hoarding had a shorter total duration of illness than those without hoarding. Newspapers and scrap were hoarded the most with sentimental reasons along with importance of goods were cited as reasons for the behaviour. The two groups showed significant differences on compulsive sub scale of the YBOCS and no differences were noted in the other scales used. Conclusion: Patients having OCD with hoarding as one of the symptoms may differ from those not having hoarding. However larger studies across diverse groups are needed to corroborate these findings.
Affect fluctuates in a moment-to-moment fashion, and it reflects the continuous relationship between the individual and the environment. Despite substantial research, there remain important open questions regarding how the continuous stream of sensory input is dynamically represented in experienced affect. Here, approaching affect as a temporally dependent process, we show that momentary affect is shaped by a combination of changes in recent stimuli (i.e. visually presented images for the current studies) and previously experienced affect. We also found that this temporally dependent relationship is influenced by context uncertainty. Participants, in each trial, viewed sequentially presented images and subsequently reported their affective experience, which was modeled based on images’ normative affect ratings and participants’ previously reported affect. Study 1 showed that self-reported valence and arousal in a given trial is partly shaped by the affective impact of the given images and previously experienced affect. In Study 2, we manipulated context uncertainty by controlling occurrence probabilities for normatively pleasant and unpleasant images in separate trials. Increasing context uncertainty (i.e. random occurrence of pleasant and unpleasant images) is associated with increased negative affect when the overall effect context is controlled. In addition, the relative contribution of the most recent image to momentary affect increased with increasing context uncertainty. Taken together, these findings provide clear behavioral evidence that affective experience fluctuates in a temporally dependent and continuous fashion based on recent changes in input variables and previous internal state, and that these fluctuations are sensitive to the affective context and its certainty.
The Plain Meaning Rule is often assailed on the grounds that it is unprincipled—that it substitutes for careful analysis an interpreter’s ad hoc and impressionistic intuition about the meaning of legal texts. But what if judges and lawyers had the means to test their intuitions about plain meaning systematically? Then initial linguistic impressions about the meaning of a legal text might be viewed as hypotheses to be tested, rather than determinative criteria upon which to base important decisions. There exists very little legal scholarship on corpus linguistics—the study of language function and use through large, electronic linguistic databases called corpora—and the role that corpus methods might play in legal interpretation. This omission becomes more and more striking as scholars and jurists (and even the United States Supreme Court) have found themselves persuaded by corpus-based arguments. This Article argues that the plain or ordinary meaning of a given term in a given context is an empirical matter that may be quantified through corpus-based methods. These methods, when applied to questions of legal ambiguity, present significant advantages over existing empirical approaches to plain meaning and over the prevailing intuition-based interpretive approach of many courts. Because large, sophisticated linguistic corpora are widely available and easy to use, and because corpus methods offer a more principled and systematic alternative to the impressionistic interpretation of legal texts, corpus linguistics may one day revolutionize the process of legal interpretation.
Nonsuicidal self-injury (NSSI; e.g., cutting or burning the skin without suicidal intent) is a dangerous and increasingly prevalent health-risk behavior. Despite advances in NSSI research over the past decade, many aspects of NSSI remain poorly understood. In particular, there are few strong predictors of NSSI, it is unclear how positive attitudes toward NSSI develop, and there are no empirically supported treatments for NSSI. In the present study, I addressed these topics with a multi-method, experimental, and longitudinal approach. For Aim 1 of the study, I examined baseline differences between NSSI (n = 58) and control (n = 86) adult participants on NSSI-themed versions of five measures that cover different aspects of attitudes: the implicit association test (IAT); the affect misattribution procedure (AMP); explicit affective ratings; startle eyeblink reactivity; and startle postauricular reactivity. Compared to the control group, the NSSI group displayed significantly more positive attitudes on all five measures. Moreover, AMP scores and explicit ratings prospectively predicted self-cutting frequency over the ensuing six months. For Aim 2, I employed pain offset relief conditioning in an attempt to induce more positive implicit attitudes toward NSSI in the control group. This conditioning significantly diminished startle eyeblink reactivity in the context of NSSI images, but did not significantly affect any other measures. For Aim 3, I tested the ability of aversive conditioning in the NSSI group to reverse positive implicit attitudes toward NSSI and to reduce NSSI behaviors over the subsequent six months. Aversive conditioning normalized startle eyeblink and postauricular reactivity, but did not significantly affect any other measures. Results also provided preliminary support for the hypothesis that aversive conditioning prospectively reduces self-cutting. In conjunction with my other recent studies (Franklin et al., 2010; 2011, 2012, 2013), these findings have prompted a new theoretical framework called the Benefits and Barriers model of NSSI.
The practice of assessing brand management in construction in Ukraine is in a passive stage, but due to the entry into the Ukrainian market of foreign companies for which regular evaluation of their brand — the need for survival in a competitive environment, Ukrainian companies are beginning to pay more attention to the creation and formation of their competitive trading because of its high business image / rating. A modern toolkit based on appropriate approaches is used to form organizational and economic foundations. The article analyzes modern scientific approaches to assessing the economic potential of an enterprise, identifies the main trends and factors that affect the assessment of construction enterprises. The article is devoted to the study of theoretical and methodological assessments of branding of construction enterprises. The role and importance of innovation in ensuring the efficient operation of modern enterprises is emphasized. It is found that construction, especially innovative, is of great social importance and has a significant economic effect. The importance of determining the potential of innovative development of construction in general and construction enterprises in particular is substantiated. The purpose of this article is to systematically investigate the interpretation of the potential of innovative development of construction enterprises. The theoretical basis of the research is the scientific works of foreign and domestic scientists on the problems of identifying the essence of innovative development potential. A systematic study of the general characteristics of the construction company brand was conducted and the priority directions for choosing the development of the economic potential of the enterprises were determined. The necessity to understand the potential of the enterprise in the unity of all its elements, which are subject to the achievement of the overall goals of the enterprise, is substantiated. The weight of the component of the brand in the potential of the construction industry enterprises is substantiated. Some aspects of the development of theoretical and methodological approaches to the estimation of the intellectual capital of construction enterprises are formulated. Existing theoretical and methodological approaches to the brand assessment of construction enterprises are analyzed.
The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words individually. Natural language understanding has seen an increasing effort in the formation of techniques that try to produce non-trivial features, in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. These new dense vector representations indeed leverage the baseline in natural language processing, but they still fall short in dealing with intrinsic issues in linguistics, such as polysemy and homonymy. Systems that make use of natural language at its core, can be affected by a weak semantic representation of human language, resulting in inaccurate outcomes based on poor decisions. In this subject, word sense disambiguation and lexical chains have been exploring alternatives to alleviate several problems in linguistics, such as semantic representation, definitions, differentiation, polysemy, and homonymy. However, little effort is seen in combining recent advances in token embeddings (e.g. words, documents) with word sense disambiguation and lexical chains. To collaborate in building a bridge between these areas, this work proposes a collection of algorithms to extract semantic features from large corpora as its main contributions, named MSSA, MSSA-D, MSSA-NR, FLLC II, and FXLC II. The MSSA techniques focus on disambiguating and annotating each word by its specific sense, considering the semantic effects of its context. The lexical chains group derive the semantic relations between consecutive words in a document in a dynamic and pre-defined manner. These original techniques' target is to uncover the implicit semantic links between words using their lexical structure, incorporating multi-sense embeddings, word sense disambiguation, lexical chains, and lexical databases. A few natural language problems are selected to validate the contributions of this work, in which our techniques outperform state-of-the-art systems. All the proposed algorithms can be used separately as independent components or combined in one single system to improve the semantic representation of words, sentences, and documents. Additionally, they can also work in a recurrent form, refining even more their results.
يعد القرآن الكريم من مصادر المعرفة، وقد تولدت منه فروع واسعة؛ إذ نُزل القرآن الكريم باللغة العربية، ولا يوجد خيار آخر لإتقان المعرفة الواردة فيه إلا من خلال تعلم اللغة العربية. تهدف هذه الدراسة إلى بيان مفهوم المدونة العربية القرآنية ومكوناتها، والكشف عن علاقة تعلم اللغة العربية بالقرآن الكريم، وبيان كيفية تعليم وتعلم القواعد العربية الأساسية عبر المدونة العربية القرآنية، وستتبع الدراسة المنهج الوصفي والتحليلي. إن وجود العلاقة بين اللغة العربية والقرآن الكريم، يدفع الطلبة المتخصصين في اللغة العربية أن يربطوا اللغة العربية بالقرآن؛ لذلك نرى أن المدونة العربية القرآنية تساعدهم على فهم القواعد القرآنية بطريقة مثيرة للاهتمام. في نظرة شاملة يمكن أن نستنتج أن المدونة العربية القرآنية هي واحدة من أهم الأدوات الحسابية التي تم إنتاجها في خدمة اللغة العربية؛ حيث توفر للمتعلمين ما يحتاجون إليه في مجال اللغة واللغويات والدراسات الحاسوبية، كما تمهد الطريق للباحثين لدراسة الهياكل المورفولوجية والنحوية من خلال دراسات الحوسبة العميقة للقرآن.
 الكلمات المفتاحية: المصرف القرآني، نموذج حاسوبي، المدونة العربية القرآنية، المعجم القرآني.
 Abstract 
 The Holy Quran is a source of knowledge and it has generated wide branches of knowledge. The Holy Quran was revealed in Arabic. Hence, there is no other option to master its knowledge except by learning the Arabic language. This study aims at explaining the concept of the Arabic Quranic Corpus and its components, revealing the relationship between learning the Arabic language and the Holy Quran, and showing how to teach and learn basic Arabic grammar through the Quranic Arabic Corpus. The study will follow the descriptive and analytical approach. The existence of the relationship between the Arabic language and the Holy Quran prompts Arabic learners to associate Arabic with the Qur'an. Therefore, we see that the Quranic Arabic Corpus helps them to understand Quranic rules in an interesting way. In a comprehensive view, we can conclude that the Arabic Quranic Corpus is one of the most important web-based medium produced to serve the Arabic language. It provides learners with what they need in the field of language, linguistics and computer studies, and paves the way for researchers to study morphological and grammatical structures through technology with detail description of grammars.
 Keywords: Quranic Treebank, Computational Model, Arabic Quranic Corpus, Qur’anic Dictionary.
Paper is dedicated to the testing of the concept of literacy, based on the questionnaire, carried out in the school year of 2018/2019 among the students of two secondary vocational schools in Vrsac, Belgrade and Grammar School in Vrsac (200 respondents). The primary hypothesis of the research was that detection and detailed study of high school students conceptosphere on literacy identify the fields to improve the teaching of Serbian as a mother tongue in secondary schools and the aim of work that, based on the collected and then processed data in analytical, cognitive and descriptive method, is to (a) isolate the dominant concepts of (non)literacy, (b) look at the tendencies of spreading and shaping the notion of literacy induced by the needs of modern life, and also that, in order to improve linguistic culture in all domains and all educational levels -(c) point to the possibility of improving the teaching of the Serbian language as a mother tongue. According to results of the survey secondary school students experience literacy in the 21 st century as a complex concept; from the one who is literate expecting linguistic knowledge, what are the basic, traditionally accepted parameters, and recognize illiteracy as the lack of ability to apply knowledge in the field of language. They also demonstrated that it is necessary to improve the efficiency of teaching approaches designed to improve functional literacy in a variety of communicative situations; increase the number of hours and exercises in the field of spelling, or nurture and acquire more comprehensive and knowledge in use and skills of different forms of literacy needed for managing in 21 st century; more attention should be paid to including relevant language handbooks in teaching; more explicit, on frequent and more familiar examples to students, point to the advantages of knowing and respecting the linguistic norm, paving the way for a better linguistic culture and enrichment of the mother tongue.
Speech processing systems rely on robust feature extraction to handle phonetic and semantic variations found in natural language. While techniques exist for desensitizing features to common noise patterns produced by Speech-to-Text (STT) and Text-to-Speech (TTS) systems, the question remains how to best leverage state-of-the-art language models (which capture rich semantic features, but are trained on only written text) on inputs with ASR errors. In this paper, we present Telephonetic, a data augmentation framework that helps robustify language model features to ASR corrupted inputs. To capture phonetic alterations, we employ a character-level language model trained using probabilistic masking. Phonetic augmentations are generated in two stages: a TTS encoder (Tacotron 2, WaveGlow) and a STT decoder (DeepSpeech). Similarly, semantic perturbations are produced by sampling from nearby words in an embedding space, which is computed using the BERT language model. Words are selected for augmentation according to a hierarchical grammar sampling strategy. Telephonetic is evaluated on the Penn Treebank (PTB) corpus, and demonstrates its effectiveness as a bootstrapping technique for transferring neural language models to the speech domain. Notably, our language model achieves a test perplexity of 37.49 on PTB, which to our knowledge is state-of-the-art among models trained only on PTB.
Lexical Markup Framework (LMF) or ISO 24613 [1] is a de jure standard that\nprovides a framework for modelling and encoding lexical information in\nretrodigitised print dictionaries and NLP lexical databases. An in-depth review\nis currently underway within the standardisation subcommittee,\nISO-TC37/SC4/WG4, to find a more modular, flexible and durable follow up to the\noriginal LMF standard published in 2008. In this paper we will present some of\nthe major improvements which have so far been implemented in the new version of\nLMF.\n
The intelligent information processing of the standard Zhuang language spoken mainly in Southern China is presently in its infancy, and lacks a well-defined language corpus and automatic part-of-speech tagging methods. Therefore, this study proposes an adversarial part-of-speech tagging method based on reinforcement learning, which solves the problems associated with a lack of a language corpus, time-consuming laborious manual marking, and the low performance of machine marking. Firstly, we construct a markup dictionary based on the grammatical characteristics of standard Zhuang and the Penn Chinese Treebank. Secondly, a dependency syntax analysis is applied for constructing the semantic information feature vectors of sentences, and long short-term memory is adopted as the policy network architecture to enhance available information using recurrent memory, and a conditional random field is employed as the discriminant network to perform label inference with global normalization. Finally, we use reinforcement learning as the model framework, target parts of speech as the feedback of the environment, and then obtain the optimal policy through adversarial learning. The results show that the combination of reinforcement learning and adversarial network alleviates the dependence of the model on the training corpus to some extent, and can quickly and effectively expand the scale of the annotation dictionary for the Zhuang language, thereby obtaining better labeling results.
According to World Intellectual Property Organization (2017) report, over 3 million patents exist in the patent database, but only certain numbers have commercial potential. Generally, to assess the commercial potential of patent, it consumes time and requires various expertise. Currently, several models have been developed to address this matter, which to assess using questionnaire tool for portfolios by human. So that occurs bias any limitation exists that our research will address by artificial intelligence. Hence, this research applies a Natural Language Programming to assess for commercial potential of patent, consisting of five steps - (i) Morphological analysis based on the Lexical database, (ii) Syntactic analysis of sentence to check syntax sentence patterns, (iii) Sematic analysis to interpret the meaning of words derived from the previous step, (iv) Discourse integration from context of domain together with the main sentence providing more accurate sentence analysis, and (v) Pragmatic analysis to ensure the correct meaning of interpretation. Then, the obtained data is used to determine criterion factors and formulate the model for assessing commercial potential of patent using Natural Language programming. This finding should deliver an alternative effective patent assessment system, which addresses some current deficiency in patent's assessment for commercial potential.
espanolResumen en castellano: Esta tesis trata de la morfologia verbal del ingles antiguo para identificar y lematizar los verbos debiles de esta lengua en un corpus al que se accede a traves de una base de datos lexica. La lematizacion es una de las tareas mas importantes a la hora de construir un diccionario. Sin embargo, es una de las tareas pendientes en el campo de la linguistica historica debido a que no existen corpora exhaustivos y lematizados de esta lengua. El enfoque de esta tesis doctoral esta en la lematizacion de las tres clases de verbos debiles del ingles antiguo, aunque las areas de la Lexicografia y la Linguistica de Corpus son tambien relevantes para esta investigacion. Las fuentes principales de esta investigacion son las formas flexivas que estan atestiguadas en el Dictionary of Old English Corpus (DOEC) y que estan disponibles en el lematizador Norna, las fuentes lexicograficas que existen publicadas sobre esta lengua, principalmente el Dictionary of Old English (DOE), y otras fuentes textuales como el York-Toronto-Helsinki Parsed Corpus of Old English (YCOE) y una indexacion de fuentes secundarias del ingles antiguo. El objetivo principal supone la identificacion de las flexiones de los verbos debiles y de su lematizacion con uno de los lemas propuestos en las listas de referencia. Conseguir este objetivo implica manejar las fuentes disponibles en ingles antiguo para poder lematizar y validar los resultados del analisis y el diseno de un metodo que combine busquedas automaticas en la base de datos lexica Nerthus y la revision manual de los resultados. La metodologia incluye cuatro pasos sucesivos con diversas tareas en cada paso. El primero de estos pasos tiene como objetivo la lematizacion de las formas canonicas de los verbos debiles lanzando cadenas de busquedas especificas para cada clase de verbos debiles en el lematizador Norna, donde esta disponible un indice de tipos del DOEC, la fuente de informacion mas fiable de la que se dispone en ingles antiguo. Despues, los resultados se validan con el DOE y se anaden las formas no-canonicas de los verbos debiles entre las letras A y H. El tercer paso tiene como objetivo identificar las formas no-canonicas de las terminaciones flexivas y de las vocales de los radicales que aparecen con mas frecuencia en los verbos debiles para generar patrones de lematizacion. La busqueda de estos patrones y de la lista de prefijos no-canonicos que esta disponible en Norna culmina en la lematizacion de las formas flexivas no transparentes de los verbos debiles. La validacion de los resultados de las letras I a la Y supone el ultimo paso de la metodologia, donde se comparan los datos obtenidos con el analisis sintactico del YCOE y con los datos que se obtienen de una base de datos de indexacion de las fuentes secundarias del ingles antiguo. Los problemas que surgen a lo largo del proceso de lematizacion tienen que ver principalmente con las peculiaridades del ingles antiguo y las limitaciones de la lematizacion de tipos que esta investigacion sigue. La discusion de los resultados del analisis concluye esta tesis. Las principales aportaciones de esta tesis son las listas de lemas y sus formas flexivas, especialmente las de los verbos entre las letras I y la Y ya que no estan disponibles todavia, y el metodo que se ha disenado para identificar estas formas, incluyendo los patrones de lematizacion generados para lematizar las formas con terminaciones no comunes y vocales no canonicas en el radical. EnglishThis thesis deals with the verbal morphology of the Old English language in order to identify and lemmatise weak verbs in a corpus accessed through a lexical database. Lemmatisation is a pending task in the field of historical linguistics given the lack of comprehensive and lemmatised corpora in this language. The focus of this doctoral dissertation is on the lemmatisation of the three classes of weak verbs, although the linguistic fields of Lexicography and Corpus Linguistics are also relevant to this research. The main aim involves the identification of the canonical and non-canonical realisations of the Old English weak verbs and their lemmatisation with a lemma from a reference list of weak verbs. Achieving this goal involves, firstly, the use of the available sources of the Old English language in order to lemmatise and validate the results and, secondly, the design of a semi-automatic research methodology that combines automatic searches in the lexical database Nerthus and the manual revision of the results in order to achieve this task. The sources for this investigation are the inflectional forms that are attested in the Dictionary of Old English Corpus (DOEC) which are available in the lemmatiser Norna, the lexicographical sources published on the Old English language, mainly the Dictionary of Old English (DOE), and other textual sources such as the York-Toronto-Helsinki Parsed Corpus of Old English (YCOE) and an index of secondary sources of Old English. The methodology comprises four successive steps and several tasks within each step. The first step aims at the lemmatisation of the transparent forms of weak verbs with the search of specific query strings for each subclass of weak verbs in the lemmatiser Norna, where an index type of the DOEC, the most reliable source of information regarding the Old English language, is available. Then, the second step validates the results with the DOE and adds to the analysis the non-canonical attestations for the weak verbs from the letter A-H. Thirdly, the identification of the most recurrent non-canonical inflectional endings and stem vowels attested in weak verbs gives rise to lemmatisation patterns. The search of these sets of correspondences and the list of non-canonical prefixes that is available in Norna results in the lemmatisation of the non-canonical inflections of weak verbs. The validation of the results from the letter I-Y concludes the research methodology with the syntactic parsing provided by the YCOE and the data retrieved from the index of secondary sources of Old English Freya. The issues that arise throughout the lemmatisation process mainly concern the idiosyncrasy of the Old English language writing system and the limitations of the lemmatisation by type that this investigation follows. The quantitative and qualitative discussion of the results of the analysis concludes this thesis. The main contributions of this thesis are the lists of weak lemmas and their lemmatised inflectional forms, specially those of the verbs I-Y which are not available yet and the designed research methodology to identify these forms, including the sets of lemmatisation patterns of the non-canonical inflectional endings and stem vowels of weak verbs.
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks.
Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER for Myanmar language has been investigated. Experiments are performed by applying deep neural network architectures on syllable level Myanmar contexts. Very first manually annotated NER corpus for Myanmar language is also constructed and proposed. In developing our in-house NER corpus, sentences from online news website and also sentences supported from ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian Language Treebank (ALT) project under ASEAN IVO. This paper contributes the first evaluation of neural network models on NER task for Myanmar language. The experimental results show that those neural sequence models can produce promising results compared to the baseline CRF model. Among those neural architectures, bidirectional LSTM network added CRF layer above gives the highest F-score value. This work also aims to discover the effectiveness of neural network approaches to Myanmar textual processing as well as to promote further researches on this understudied language.