Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Similarity calculation between business process models has an important role in managing repository of business process model. One of its uses is to facilitate the searching process of models in the repository. Business process similarity is closely related to semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database such as WordNet to find the semantic meaning of the word. The activity name of the business process uses terms that specifically related to the business field. However, most of the terms in business domain are not available in WordNet. This case would decrease the semantic analysis quality of business process model. Therefore, this study would try to improve semantic analysis of business process model. We present a new lexical database called B-BabelNet. B-BabelNet is a lexical database built by using the same method in BabelNet. We attempt to map the Wikipedia page to WordNet database but only focus on the word related to the domain of business. Also, to enrich the vocabulary in the business domain, we also use terms in the business-specific online dictionary (businessdictionary.com). We utilize this database to do word sense disambiguation process on business process model activity’s terms. The result from this study shows that the database can increase the accuracy of the word sense disambiguation process especially in particular terms related to the business and industrial domains.
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and With our minimal features, we also present Opn 3 q global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the "second-best-in-class" result on the English Penn Treebank.
Positive emotional perceptions and healthy emotional intelligence (EI) are important for social functioning. In this study, we investigated whether loving kindness meditation (LKM) combined with anodal transcranial direct current stimulation (tDCS) would facilitate improvements in EI and changes in affective experience of visual stimuli. LKM has been shown to increase positive emotional experiences and we hypothesized that tDCS could enhance these effects. Eighty-seven undergraduates were randomly assigned to 30 minutes of LKM or a relaxation control recording with anodal tDCS applied to the left dorsolateral prefrontal cortex (left dlPFC) or right temporoparietal junction (right TPJ) at 0.1 or 2.0 milliamps. The primary outcomes were self-reported affect ratings of images from the International Affective Picture System and EI as measured by the Mayer, Salovey and Caruso Emotional Intelligence Test. Results indicated no effects of training on EI, and no main effects of LKM, electrode placement, or tDCS current strength on affect ratings. There was a significant interaction of electrode placement by meditation condition (p = 0.001), such that those assigned to LKM and right TPJ tDCS, regardless of current strength, rated neutral and positive images more positively after training. Results suggest that LKM may enhance positive affective experience.
In this paper, we attempt a comparison between new school transition-based parsers that use neural networks and their classical old school coun-terpart. We carry out experiments on treebanks fr...
Discourse-annotated corpora are an important resource for the community. However, these corpora are often annotated according to different frameworks, making comparison of the annotations difficult. This is unfortunate, since mapping the existing annotations would result in more (training) data for researchers in automatic discourse relation processing and researchers in linguistics and psycholinguistics. In this article, we present an effort to map two large corpora onto each other: the Penn Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank. We first propose a method for aligning the discourse segments, and then evaluate the observed against the expected mappings for explicit and implicit relations separately. We find that while agreement on explicit relations is reasonable, agreement between the frameworks on implicit relations is astonishingly low. We identify sources of systematic discrepancies between the two annotation schemes; many of the differences in annotation can be traced back to different operationalizations and goals of the PDTB and RST frameworks. We discuss the consequences of these discrepancies for future annotation, and the usability of the mapped data for theoretical studies and the training of automatic discourse relation labellers.
We present a method for automatically converting the Dutch Lassy Small treebank, a phrasal dependency treebank, to UD. All of the information required to produce accurate UD annotation appears to be available in the underlying annotation. However, we also note that the close connection between POS-tags and dependency labels that is present in UD is missing in the Lassy treebanks. As a consequence, annotation decisions in the Dutch<br/>data for such phenomena as nominalization<br/>and clausal complements of prepositions<br/>seem to differ to some extent from comparable data in English and German. Because the conversion is automatic, we can now also compare three state-of-theart dependency parsers trained on UD Lassy Small with Alpino, a hybrid Dutch parser which produces output that is compatible with the original Lassy annotations.
Part-of-speech (POS) tagging for morphologically rich languages such as Arabic is a challenging problem because of their enormous tag sets. One reason for this is that in the tagging scheme for such languages, a complete POS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category. Previous approaches in Arabic POS tagging applied one model for each morphosyntactic tagging task, without utilizing shared information between the tasks. In this paper, we propose an approach that utilizes this information by jointly modeling multiple morphosyntactic tagging tasks with a multi-task learning framework. We also propose a method of incorporating tag dictionary information into our neural models by combining word representations with representations of the sets of possible tags. Our experiments showed that the joint model with tag dictionary information results in an accuracy of 91.38% on the Penn Arabic Treebank data set, with an absolute improvement of 2.11% over the current state-of-the-art tagger. 1
We compare several language models for the word-ordering task and propose a new bag- to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.
Causal relations play a key role in information extraction and reasoning. Most of the times, their expression is ambiguous or implicit, i.e. without signals in the text. This makes their identification challenging. We aim to improve their identification by implementing a Feedforward Neural Network with a novel set of features for this task. In particular, these are based on the position of event mentions and the semantics of events and participants. The resulting classifier outperforms strong baselines on two datasets (the Penn Discourse Treebank and the CSTNews corpus) annotated with different schemes and containing examples in two languages, English and Portuguese. This result demonstrates the importance of events for identifying discourse relations.
Svetlana Toldova, Dina Pisarevskaya, Margarita Ananyeva, Maria Kobozeva, Alexander Nasedkin, Sofia Nikiforova, Irina Pavlova, Alexey Shelepov. Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms. 2017.
Long short-term memory (LSTM) has been widely used in different applications, such as natural language processing, speech recognition, and computer vision over recurrent neural network (RNN) or recursive neural network (RvNN)-a tree-structured RNN. In addition, the LSTM-RvNN has been used to represent compositional semantics through the connections of hidden vectors over child units. However, the linear connections in the existing LSTM networks are incapable of capturing complex semantic representations of natural language texts. For example, complex structures in natural language texts usually denote intricate relationships between words, such as negated sentiment or sentiment strengths. In this paper, quadratic connections of the LSTM model is proposed in terms of RvNNs (abbreviated as qLSTM-RvNN) in order to attack the problem of representing compositional semantics. The proposed qLSTM-RvNN model is evaluated in the benchmark data sets containing semantic compositionality, i.e., sentiment analysis on Stanford Sentiment Treebank and semantic relatedness on sentences involving compositional knowledge data set. Empirical results show that it outperforms the state-of-the-art RNN, RvNN, and LSTM networks in two semantic compositionality tasks by increasing the classification accuracies and sentence correlation while significantly decreasing computational complexities.
In this paper, a deep phrase embedding approach using bi-directional long short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a local order-aware word, a global phrase embedding representations and a deep regression neural network (DRNN) model. The performance of the proposed method was benchmarked by the IJCNLP 2017 shared task 2. According the official evaluation results, our best system achieved mean rank 6.5 among all 24 submissions.
Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem are pipelined rather than end-to-end, sophisticated, and not self-contained: they assume goldstandard text segmentations (Elementary Discourse Units), and use external parsers for syntactic features.
Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, word affect features, which derive human affect ratings (e.g. arousal and valence) from transcripts, were investigated for predicting depression severity and liking, showing great promise. A simple system based on the word affect features achieved an RMSE of 6.02 on the test set, yielding a relative improvement of 13.6% over the baseline. For the emotion prediction sub-challenge, we investigated multimodal fusion, which incorporated a measure of uncertainty associated with each prediction within an Output-Associative fusion framework for arousal and valence prediction, whilst liking prediction systems mainly focused on text-based features. Our best emotion prediction systems provided significant relative improvements over the baseline on the test set of 39.5%, 17.6%, and 29.3% for arousal, valence, and liking. Of particular note is that consistent improvements were observed when incorporating prediction uncertainty across various system configurations for predicting arousal and valence, suggesting the importance of taking into consideration prediction uncertainty for fusion and more broadly the advantages of probabilistic predictions.
Causal relations play a key role in information extraction and reasoning. Most of the times, their expression is ambiguous or implicit, i.e. without signals in the text. This makes their identification challenging. We aim to improve their identification by implementing a Feedforward Neural Network with a novel set of features for this task. In particular, these are based on the position of event mentions and the semantics of events and participants. The resulting classifier outperforms strong baselines on two datasets (the Penn Discourse Treebank and the CSTNews corpus) annotated with different schemes and containing examples in two languages, English and Portuguese. This result demonstrates the importance of events for identifying discourse relations.
This paper introduces the Universal Dependencies Treebank for Slovenian. We overview the existing dependency treebanks for Slovenian and then detail the conversion of the ssj200k treebank to the framework of Universal Dependencies version 2. We explain the mapping of part-of-speech categories, morphosyntactic features, and the dependency relations, focusing on the more problematic language-specific issues. We conclude with a quantitative overview of the treebank and directions for further work.
In this paper, we propose efficient and less resource-intensive strategies\nfor parsing of code-mixed data. These strategies are not constrained by\nin-domain annotations, rather they leverage pre-existing monolingual annotated\nresources for training. We show that these methods can produce significantly\nbetter results as compared to an informed baseline. Besides, we also present a\ndata set of 450 Hindi and English code-mixed tweets of Hindi multilingual\nspeakers for evaluation. The data set is manually annotated with Universal\nDependencies.\n
OBJECTIVE: Ecological momentary assessment (EMA) research has produced contradictory findings regarding the trajectory of negative affect after binge-eating episodes. Given the clinical implications, the objective of the current study was to reconcile these inconsistencies by comparing the two most commonly employed statistical approaches used to analyze these data. METHOD: Data from two EMA studies were analyzed separately. Study 1 included 118 adult females with full- or subthreshold DSM-IV anorexia nervosa. Study 2 included 131 adult females with full-threshold DSM-IV bulimia nervosa. For each dataset, the single most proximal negative affect ratings preceding and following a binge-eating episode were compared. The times at which these ratings were made, relative to binge-eating episodes, were also compared. RESULTS: The results indicate that the average proximal pre-binge ratings of negative affect were significantly higher than the average proximal post-binge ratings of negative affect. However, results also indicate that the average proximal post-binge ratings of negative affect were made significantly closer in time to the binge-eating episodes (∼20 min post-binge) than the average proximal pre-binge ratings of negative affect (∼2.5 hr pre-binge). A graphical representation of the results demonstrates that the average proximal pre-binge and post-binge ratings map closely onto the results of previous studies. DISCUSSION: These data provide one possible explanation for the inconsistent findings regarding the trajectory of negative affect after binge eating. Moreover, they suggest that the findings from previous studies are not necessarily contradictory, but may be complementary, and appear to bolster support for the affect regulation model of binge eating.
OBJECTIVE: Intrusive negative affect and concurrent deficits in positive affect are hallmarks of posttraumatic stress disorder (PTSD). We sought to further extend the extant literature by exploring the experience of negative affect intrusion upon potentially positive situations (here termed, "negative affect interference," NAI). METHOD: Two studies with adults endorsing at least 1 traumatic event (Study 1, N = 294; Study 2, N = 286) examined how NAI and more general hedonic deficits (HD) relate to psychopathology, trauma exposure characteristics, and ratings of normed visual stimuli. RESULTS: Study 1 found that NAI and HD were positively correlated with PTSD symptoms and childhood trauma, and NAI incremented over depressive symptoms in predicting PTSD severity. Study 2 results indicated additional strong positive correlations between NAI and HD and anhedonia, affect regulation problems, negative affect, and neuroticism. NAI and HD were found to increment over trait NA in predicting PTSD symptoms. Individuals endorsing elevated NAI and HD rated positively valenced pictures (including food and erotic images) as less arousing, although not more negative. CONCLUSIONS: These findings expand conceptualizations of anhedonia and emotional numbing by drawing attention to negative affect in otherwise positive contexts. (PsycINFO Database Record
International audience
We describe the new version of GrETEL (http://gretel.ccl.kuleuven.be/gretel3), an online tool which allows users to query treebanks by means of a natural language example (example-based search) or via a formal query (XPath search). The new release comprises an update to the interface and considerable improvements in the back-end search mechanism. The update of the front-end is based on user suggestions. In addition to an overall design update, major changes include a more intuitive query builder in the example-based search mode and a visualizer for syntax trees that is compatible with all modern browsers. Moreover, the results are presented to the user as soon as they are found, so users can browse the matching sentences before the treebank search is completed. We will demonstrate that those changes considerably improve the query procedure. The update of the back-end mainly includes optimizing the search algorithm for querying the (very) large SoNaR treebank. Querying this 500-million word treebank was already made possible in the previous version of GrETEL, but due to the complex search mechanism this often resulted in long query times or even a timeout before the search completed. The improved version of the search algorithm results in faster query times and more accurate search results, which greatly enhances the usability of the SoNaR treebank for linguistic research.
It has been found that the length distribution of many linguistic units fits well the same model, the Zipf-Alekseev function. In this article, we aimed to find out whether this holds for English learners’ interlanguage and whether the parameters in probability distribution of dependency distance can measure the language proficiency of second language learners. We selected 367 participants of English learners of nine consecutive grades and fitted different probability distribution models to dependency distances of their writings in English and of self-built contrastive dependency treebanks based on Wall Street Journal Corpus. It was found that: (1) the Zipf-Alekseev distribution well captures the probability distribution of dependency distance of each grade and native speakers; (2) the probability distribution of dependency distance well measures second language learners’ language proficiency at different learning stages; (3) high-level learners don’t present exactly the same parameters in the probability distribution of dependency distance as those of native speakers, which means learners’ language proficiency is not as high as that of English native speakers and second language learners’ syntactic acquisition process is always constrained by the tendency of dependency distance minimization. This study corroborates that quantitative linguistic methods can be well utilized in second language acquisition researches.
The major mode conveys positive emotion, whereas the minor mode conveys negative emotion. However, previous studies have primarily focused on the emotions induced by Western music in Western participants. The influence of the musical mode (major or minor) on Chinese individuals’ perception of Western music is unclear. In the present experiments, we investigated the effects of musical mode and harmonic complexity on psychological perception among Chinese participants. In Experiment 1, the participants (N = 30) evaluated 24 musical excerpts in five dimensions (pleasure, arousal, dominance, emotional tension and liking). In Experiment 2, the participants (N = 40) evaluated 48 musical excerpts. Perceptions of the musical excerpts differed significantly according to mode, even if the stimuli were Western musical excerpts. The major-mode music induced greater pleasure and arousal and produced higher liking ratings than the minor-mode music, whereas the minor-mode music induced greater tension than the major-mode music. Mode did not influence the dominance rating. Perception of Western music was not influenced by harmonic complexity. Moreover, preference for musical mode was influenced by previous exposure to Western music. These results confirm the cross-cultural emotion induction effects of musical modes in Western music.
Abstract Punctuated equilibrium theory (PET) suggests that the policy process is characterized by long periods of incremental change and short periods of punctuated change. The impetus for the latter is usually a focusing event that breaks open policy monopolies, allowing for major changes in legislative decision making. While a burgeoning body of literature, a shortcoming in the PET literature is that it has yet to explain why focusing events and subsequent breakdowns in policy monopolies sometimes fail to result in punctuated policy. We integrate theories on cultural change with punctuated equilibrium to explain why focusing events do not always result in the dramatic policy changes that we might expect. Specifically, we use the context of national energy policy and the lexical database, Google Ngram Viewer, to trace punctuating energy‐related events and the occurrence or lack thereof subsequent policy change from 1952 to 2000.
Accurate sentiment analysis models encode the sentiment of words and their combinations to predict the overall sentiment of a sentence. This task becomes challenging when applied to morphologically rich languages (MRL). In this article, we evaluate the use of deep learning advances, namely the Recursive Neural Tensor Networks (RNTN), for sentiment analysis in Arabic as a case study of MRLs. While Arabic may not be considered the only representative of all MRLs, the challenges faced and proposed solutions in Arabic are common to many other MRLs. We identify, illustrate, and address MRL-related challenges and show how RNTN is affected by the morphological richness and orthographic ambiguity of the Arabic language. To address the challenges with sentiment extraction from text in MRL, we propose to explore different orthographic features as well as different morphological features at multiple levels of abstraction ranging from raw words to roots. A key requirement for RNTN is the availability of a sentiment treebank; a collection of syntactic parse trees annotated for sentiment at all levels of constituency and that currently only exists in English. Therefore, our contribution also includes the creation of the first Arabic Sentiment Treebank (A r S en TB) that is morphologically and orthographically enriched. Experimental results show that, compared to the basic RNTN proposed for English, our solution achieves significant improvements up to 8% absolute at the phrase level and 10.8% absolute at the sentence level, measured by average F1 score. It also outperforms well-known classifiers including Support Vector Machines, Recursive Auto Encoders, and Long Short-Term Memory by 7.6%, 3.2%, and 1.6% absolute respectively, all models being trained with similar morphological considerations.
BACKGROUND: Apart from a progressive decline of motor functions, Parkinson's disease (PD) is also characterized by non-motor symptoms, including disturbed processing of emotions. This study aims at assessing emotional processing and its neurobiological correlates in PD with the focus on how medicated Parkinson patients may achieve normal emotional responsiveness despite basal ganglia dysfunction. METHODS: Nineteen medicated patients with mild to moderate PD (without dementia or depression) and 19 matched healthy controls passively viewed positive, negative, and neutral pictures in an event-related blood oxygen level-dependent functional magnetic resonance imaging study (BOLD-fMRI). Individual subjective ratings of valence and arousal levels for these pictures were obtained right after the scanning. RESULTS: Parkinson patients showed similar valence and arousal ratings as controls, denoting intact emotional processing at the behavioral level. Yet, Parkinson patients showed decreased bilateral putaminal activation and increased activation in the right dorsomedial prefrontal cortex (PFC), compared to controls, both most pronounced for highly arousing emotional stimuli. CONCLUSIONS: Our findings revealed for the first time a possible compensatory neural mechanism in Parkinson patients during emotional processing. The increased medial PFC activity may have modulated emotional responsiveness in patients via top-down cognitive control, therewith restoring emotional processing at the behavioral level, despite striatal dysfunction. These results may impact upon current treatment strategies of affective disorders in PD as patients may benefit from this intact or even compensatory influence of prefrontal areas when therapeutic strategies are applied that rely on cognitive control to modulate disturbed processing of emotions.
While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
In recent years, the research on Treebank has made great progress. However, the application of the Treebank research in international Chinese teaching is not very satisfactory. In view of international Chinese teaching, this paper constructs a diagrammatic Treebank based on the Li Jinxi's Sentence-based Grammar. With the constructing of the diagrammatic Treebank, we have made an exploration in word interpretation based on context, accurate example sentences recommendations based on word senses, words exercise based on dynamic word patterns, and specific grammar point example sentences recommendation.
Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7-7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.
This paper addresses the problem of sentence-level sentiment analysis. In recent years, Convolution and Recursive Neural Networks have been proven to be effective network architecture for sentence-level sentiment analysis. Nevertheless, each of them has their own potential drawbacks. For alleviating their weaknesses, we combined Convolution and Recursive Neural Networks into a new network architecture. In addition, we employed transfer learning from a large document-level labeled sentiment dataset to improve the word embedding in our models. The resulting models outperform all recent Convolution and Recursive Neural Networks. Beyond that, our models achieve comparable performance with state-of-the-art systems on Stanford Sentiment Treebank.
This article describes a model of otherinitiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database.
We describe the process of creating NUDAR, a Universal Dependency treebank for Arabic. We present the conversion from the Penn Arabic Treebank to the Universal Dependency syntactic representation through an intermediate dependency representation. We discuss the challenges faced in the conversion of the trees, the decisions we made to solve them, and the validation of our conversion. We also present initial parsing results on NUDAR.
This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for lowresource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).
We employ a novel paradigm to test whether six basic emotions (sadness, fear, disgust, anger, surprise, and happiness; Ekman, 1992) contain both negativity and positivity, as opposed to consisting of a single continuum between negative and positive. We examined the perceived negativity and positivity of these emotions in terms of their affective and cognitive components among Korean, Chinese, Canadian, and American students. Assessing each emotion at the cognitive and affective levels cross-culturally provides a fairly comprehensive picture of the positivity and negativity of emotions. Affective components were rated as more divergent than cognitive components. Cross-culturally, Americans and Canadians gave higher valence ratings to the salient valence of each emotion, and lower ratings to the non-salient valence of an emotion, compared to Chinese and Koreans. The results suggest that emotions encompass both positivity and negativity, and there were cross-cultural differences in reported emotions. This paradigm complements existing emotion theories, building on past research and allowing for more parsimonious explanations of cross-cultural research on emotion.
Previous findings indicate that negative arousal enhances bottom-up attention biases favouring perceptual salient stimuli over less salient stimuli. The current study tests whether those effects were driven by emotional arousal or by negative valence by comparing how well participants could identify visually presented letters after hearing either a negative arousing, positive arousing or neutral sound. On each trial, some letters were presented in a high contrast font and some in a low contrast font, creating a set of targets that differed in perceptual salience. Sounds rated as more emotionally arousing led to more identification of highly salient letters but not of less salient letters, whereas sounds' valence ratings did not impact salience biases. Thus, arousal, rather than valence, is a key factor enhancing visual processing of perceptually salient targets.
Dynamically tracking changes in affective responses to continuous streams of differently valenced stimuli
The means of verbalization of the concept of CHALLENGE and the functioning of its lexical representatives in the texts of glossy magazines are considered. The conceptual, axiological and figurative components of the concept are studied by the method of conceptual analysis. It is noted that the concept under study has a large number of verbal representatives, characterized by the originality of the semantic and structural peculiarities in texts about fashion. A comprehensive study of the concept of CHALLENGE is necessary in order to ensure the effectiveness of the impact on potential buyers - recipients of “fashionable” product. The analysis of lexical means and ways of presentation of this concept in the fashion discourse creates the basis to explore means of speech manipulation by the consciousness of the target audience to change and correct beliefs and attitudes of its representatives. Axiological component of the studied concept is commented. The author argues that the main structural elements of the conceptual component of the concept of CHALLENGE in texts of glossy magazines are “a challenge to the norms of behavior in society,” “challenge to the fashion,” “challenge to myself.” These data make a presentation about one of the most popular concepts prevalent in the minds of readers of glossy magazines.
The paper presents the project for creation of lexical database that is intended to include the Bulgarian and Czech neologisms. It describes the principles for selection of lexical units which should be included in the database as well as the resources of the lexical material. The paper presents also the database structure and the content of its modules and submodules.
We define the notion of controlled hybrid language that allows information share and interaction between a controlled natural language (specified by a context-free grammar) and a controlled visual language (specified by a Symbol-Relation grammar). We present the controlled hybrid language INAUT, used to represent nautical charts of the French Naval and Hydrographic Service (SHOM) and their companion texts (Instructions nautiques).
One of the core challenges for building the semantic web is the creation of ontologies, a process known as ontology authoring. Controlled natural languages (CNLs) propose different frameworks for interfacing and creating ontologies in semantic web systems using restricted natural language. However, in order to engage non-expert users with no background in knowledge engineering, these language interfacing must be reliable, easy to understand and accepted by users. This paper includes the state-of-the-art for CNLs in terms of ontology authoring and the semantic web. In addition, it includes a detailed analysis of user evaluations with respect to each CNL and offers analytic conclusions with respect to the field.
We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the “face space” produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.
Human action perception is so powerful that people can identify movement efficiently in the absence of pictorial information, such as in point-light displays. Interest is growing in this type of stimulus for research in neuroscience. This interest stems from the advantage of separating the component of pure human action kinematics from other pictorial information, such as facial expression and muscle contraction. Although several groups have previously developed datasets of human point-light actions, due to the lack of datasets composed of daily actions with short durations, we developed 20 biological and 40 control (scrambled) point-light movements by using the technique of recording people wearing reflector patches. The videos are about 1 s long. Subsequently, we performed a judgment task in which 100 participants (50 male and 50 female) evaluated each video according to three categories: human action resemblance, performed action, and gender of actor. We present the mean scores of each evaluation for each video, and further propose a selection of the most suitable videos to be used as human point-light action displays and scrambled point-light displays for control. Finally, we discuss our findings on the gender attributions of the point-light displays.
One thousand one hundred and twenty subjects as well as a developmental phonagnosic subject (KH) along with age-matched controls performed the Glasgow Voice Memory Test, which assesses the ability to encode and immediately recognize, through an old/new judgment, both unfamiliar voices (delivered as vowels, making language requirements minimal) and bell sounds. The inclusion of non-vocal stimuli allows the detection of significant dissociations between the two categories (vocal vs. non-vocal stimuli). The distributions of accuracy and sensitivity scores (d’) reflected a wide range of individual differences in voice recognition performance in the population. As expected, KH showed a dissociation between the recognition of voices and bell sounds, her performance being significantly poorer than matched controls for voices but not for bells. By providing normative data of a large sample and by testing a developmental phonagnosic subject, we demonstrated that the Glasgow Voice Memory Test, available online and accessible from all over the world, can be a valid screening tool (~5 min) for a preliminary detection of potential cases of phonagnosia and of “super recognizers” for voices.
The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response’s latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization.