Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Ratings of previously ignored visual stimuli reveal affective devaluation of such items when compared to ratings of novel items or the targets of attention. Growing evidence suggests this effect may reflect negative affective associations elicited by attentional inhibition of visual distractors. Here we investigate whether such 'inhibitory devaluation' is limited to situations involving visual-spatial selection of environmental stimuli (i.e., external attention) or extends to the selection of competing visual representations held solely in memory (i.e., internal attention). A two-item target-localization task in Experiment 1 utilized a delayed target-category cue ('circles' or 'squares') to ensure attentional selection occurred from the contents of working memory. An n-back task in Experiment 2 was used to examine the affective consequences of rejecting continually-updated visual representations when items held in memory did not match the corresponding visual display. And a Think/No-think paradigm employed in Experiment 3 was designed to explore the affective consequences of actively suppressing longer-term visual object memories. Across this relatively-wide range of memory-based selection tasks, the ignored/rejected/suppressed visual patterns consistently received more negative affective ratings than target items. Our results are consistent with prior suggestions that similar mechanisms are involved in the attentional selection of environmental stimuli and the selection of internally-maintained information that occurs even in the absence of external sensory stimulation. The similarity in these mechanisms appears to extend not only to processes of attentional selection, per se, but also to their affective consequences. Meeting abstract presented at VSS 2014
Using fMRI, we investigated the behavioral and neural consequences of Compassion meditation when employed as an emotion regulation strategy and compared this to Reappraisal based cognitive emotion regulation. 15 expert meditators were scanned while either passively viewing, or using Compassion meditation or Reappraisal to regulate their emotional reactions to short film clips depicting people in distress. Subjective affect ratings showed that Compassion meditation primarily increased positive affect while Reappraisal primarily decreased negative affect. Neuroimaging results showed that the Compassion vs. Passive Viewing contrast was associated with increased activation in regions involved in affiliation and positive affect (ventral striatum, mOFC), in addition to cognitive (left IFG, TPJ, pre-SMA) and affective (sgACC) control regions. Mirroring behavioral results, the Compassion vs Reappraisal contrast showed higher activation in regions involved in negative (amygdala, insula) and positive (ventral striatum, mOFC) affect and emotional control regions (sgACC). Relatively lower activation was observed in cognitive control regions (Frontoparietal network, IFG). Our findings demonstrate the efficacy of Compassion meditation as an emotion regulation strategy, suggesting that the active regulatory mechanism of Compassion is primarily the up-regulation of positive affect. Thus, Compassion is markedly different from other coping strategies in relying less on cognitive effort when faced with stressors, suggesting it could be powerful strategy for the fostering of resilience.
This study presents a comparison of the themes associated with the phenomena of birth and death in Pakistani and British English fictions i.e. PEF and BEF in the vast framework of several varieties of Englishes around the world. As culture of Pakistan differs entirely from that of Britain, so are the themes which are associated with birth and death in their fictions. Different words have different associative meaning in different societies (e.g. meaning and usage of ‘dear’ or ‘clever’ is quite different in Pakistan and in England. Truly representative corpus of English fiction, comprising various genres from both the varieties of English has been analyzed and processed through AntConc 3.2.2w (windows) 2008. The study has investigated PEF as well as BEF thoroughly on the basis of Dixon’s Semantic approach to English grammar (2005) and elaborated that the adjectives used with birth and death in both fictions are entirely different besides the universally common psychological incidents of birth and death in all human beings. The study reveals that the rituals and customs associated with birth and death are entirely different in BEF and PEF. It establishes that Pakistani variety of English uses entirely distinctive linguistic norms as compared to British variety of English. Keywords: PEF, BEF, themes, birth, death, associative meaning.
This paper showcases development on a digital learning environment built for users interested in ancient languages. The environment adopts a two-pronged approach: first, it assesses user progress during the language learning phase by using exercises such as treebanking and alignment; then, it offers users the opportunity to contribute their own annotated data. This paper provides a description of user testing conducted by the eLearning team in order to understand how these methods affect user engagement and persistence. The results of user testing showed higher user engagement and retention rates when compared to a more traditional quizzing method.
We describe a contextual parser for the Robot Commands Treebank, a new crowdsourced resource. In contrast to previous semantic parsers that select the most-probable parse, we consider the different problem of parsing using additional situational context to disambiguate between different readings of a sentence. We show that multiple semantic analyses can be searched using dynamic programming via interaction with a spatial planner, to guide the parsing process. We are able to parse sentences in near linear-time by ruling out analyses early on that are incompatible with spatial context. We report a 34% upper bound on accuracy, as our planner correctly processes spatial context for 3,394 out of 10,000 sentences. However, our parser achieves a 96.53% exact-match score for parsing within the subset of sentences recognized by the planner, compared to 82.14% for a non-contextual parser.
The syntactic ambiguity of a transitive verb (Vt) followed by a noun (N) has long been a problem in Chinese parsing. In this paper, we propose a classifier to resolve the ambiguity of Vt-N structures. The design of the classifier is based on three important guidelines, namely, adopting linguistically motivated features, using all available resources, and easy in-tegration into a parsing model. The lin-guistically motivated features include semantic relations, context, and morpho-logical structures; and the available re-sources are treebank, thesaurus, affix da-tabase, and large corpora. We also pro-pose two learning approaches that resolve the problem of data sparseness by auto-parsing and extracting relative knowledge from large-scale unlabeled data. Our experiment results show that the Vt-N classifier outperforms the cur-rent PCFG parser. Furthermore, it can be easily and effectively integrated into the PCFG parser and general statistical pars-ing models. Evaluation of the learning approaches indicates that world knowledge facilitates Vt-N disambigua-tion through data selection and error cor-rection. 1
Taboada et al. (2008) propose a word-based method for extracting sentiment from text that relies on the most relevant parts of a text. The method predicts that opinion words found in the nuclei (more important parts) of a document are more significant for the overall sentiment, whereas opinion words found in the satellites (less important parts) only potentially interfere with the overall sentiment. However, as pointed out by Taboada et al. (2008) and Narayanan et al. (2009), for certain discourse relations (for instance, Condition relations), the calculation of sentiment should involve both parts of the relation. Based on our analysis of the affective content expressed by automatically extracted discourse relations from the Simon Fraser University Corpus (Taboada 2008) and the Penn Discourse Treebank (Prasad et al. 2008), we propose to classify all the discourse relations into four categories: (1) relations that reverse polarity, (2) intensify polarity, (3) downtone polarity, or (4) produce no change in polarity. We compare the performance of a sentiment analysis system (SO-CAL, Taboada et al. 2011) when opinion words are detected only in the nuclei with its performance when both parts of the relation are analyzed in combination with the opinion words. The results of the experiment show that extraction of both the nucleus and the satellite parts of texts does not improve the performance of a sentiment extraction system.
Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. Word representation is the most important determination of similarity. Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed. The word representation is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word. For learning of the representations, a multi-word-agent autonomous learning model (MWAALM) based on an artificial immune system is presented. This research provides a completely new perspective on language and words. The most significant advantages of this research lie in two aspects: the first is that lymphocyte-style word representation can express both similarities and dependency relations between words, the second is that the MWAALM is implemented concisely and has the potential ability of continuous learning since the simulated targets have the ability of adaptation. Lymphocyte-style word representations are evaluated by computing the similarities between words, and experiments are conducted on the Penn Chinese Treebank 5.1. Experimental results indicate that the proposed word representations are effective.
The present sociophonetic study examines the English variety in Michigan's Upper Peninsula (UP) based upon a 130-speaker sample from Marquette County. The linguistic variables of interest include seven monophthongs and four diphthongs: 1) front lax, 2) low back, and 3) high back monophthongs and 4) short and 5) long diphthongs. The sample is stratified by the predictor variables of heritage-location, bilingualism, age, sex and class. The aim of the thesis is two fold: 1) to determine the extent of potential substrate effects on a 71-speaker older-aged bilingual and monolingual subset of these UP English speakers focusing on the predictor variables of heritage-location and bilingualism, and 2) to determine the extent of potential exogenous influences on an 85-speaker subset of UP English monolingual speakers by focusing on the predictor variables of heritage-location, age, sex and class. All data were extracted from a reading passage task collected during a sociolinguistic interview and measured instrumentally. The findings of this apparent-time data reveal the presence of lingering effects from substrate sources and developing effects from exogenous sources based upon American and Canadian models of diffusion. The linguistic changes-in-progress from above, led by middle-class females, are taking shape in the speech of UP residents of whom are propagating linguistic phenomena typically associated with varieties of Canadian English (i.e., low-back merger, Canadian shift, and Canadian raising); however, the findings also report resistance of such norms by working-class females. Finally, the data also reveal substrate effects demonstrating cases of dialect leveling and maintenance. As a result, the speech spoken in Michigan's Upper Peninsula can presently be described as a unique variety of English comprised of lingering substrate effects as well as exogenous effects modeled from both American and Canadian English linguistic norms.
In current web applications, more businesses are gradually publishing their business as services over the web. This growing number of web services available within an organization and on the Web raises a new and challenging search problem: locating desired web services. Searching for web services with conventional web search engines is insufficient in this context. Automatically clustering Web Service Description Language (WSDL) files on the web into functionally similar homogeneous service groups can be seen as a bootstrapping step for creating a service search engine and, at the same time, reducing the search space for service discovery. In order to overcome some the limitations of pattern-matching approach, the proposed work uses two semantic approaches to cluster similar services. An experimental study based on an information retrieval technique known as latent semantic analysis is applied to the collection of WSDL files and the another semantic approach is based on WordNet which is a lexical database to cluster similar services, as a predecessor step to retrieve the relevant Web services for a user request by search engines. The baseline approach and the two approaches based on semantic is applied on a collection of WSDL documents consisting of to test the quality of clusters formed. As a result, WordNet based approach for clustering shows better cluster quality.
We propose a novel approach for learning image representation based on qualitative assessments of visual aesthetics. It relies on a multi-node multi-state model that represents image attributes and their relations. The model is learnt from pair wise image preferences provided by annotators. To demonstrate the effectiveness we apply our approach to fashion image rating, i.e., comparative assessment of aesthetic qualities. Bag-of-features object recognition is used for the classification of visual attributes such as clothing and body shape in an image. The attributes and their relations are then assigned learnt potentials which are used to rate the images. Evaluation of the representation model has demonstrated a high performance rate in ranking fashion images.
Resumen Este artículo analiza las actitudes lingüísticas de hablantes nativos de español de la Ciudad Autónoma de Buenos Aires, hacia al español de la Argentina y el español de los otros países hispanohablantes. El artículo es parte de los resultados del Proyecto LIAS (Linguistic Identity and Attitudes in Spanish-speaking Latin America), financiado por El Consejo Noruego de Investigación (RCN). La recolección de los datos se realizó en la capital del país, entrevistando a una muestra de 400 informantes previamente estratificada con las variables de edad, sexo y nivel socioeconómico. El procesamiento estadístico de los datos de campo recolectados arrojó resultados de interés en torno a la mayoría de los tópicos analizados y especialmente en lo referente a aspectos tales como la valoración positiva de la propia variedad lingüística; la resistencia a identificar a España como la única fuente de la norma lingüística de la lengua española; el rechazo a la unificación de la lengua y, por consiguiente, la defensa de la diversidad lingüística como portadora de riqueza cultural. Abstract This article analyzes the linguistic attitudes of native Spanish speakers from Buenos Aires City, towards Spanish spoken in Argentina and in the other Spanish-speaking countries. It is a result of the LIAS-Project (Linguistic Identity and Attitudes in Spanish-speaking Latin America), funded by The Research Council of Norway (RCN). The data were gathered in the capital of the country, interviewing a stratified sample of 400 respondents, based on the variables of age, sex and socioeconomic status. The analysis of the data rendered interesting results on most of the analyzed topics; especially important was the positive appraisal of Argentineans' own linguistic variety; the strong resistance against identifying Spain as the only source of the linguistic norm for the Spanish language; and the rejection of language unification, defending in this way linguistic diversity as an important conveyor of cultural richness.
The authors focus on how to segment semantic units in Chinese discourse and how to label relations among semantic units automatically. During the parsing process, several sequence labelling methods are compared for discourse segmentation, while a maximum entropy-based training and decoding algorithm is specially proposed. Experiments are done based on Tsinghua Chinese Treebank, which is annotated with logical and semantic relations at complex-sentence level. Experimental results show that F-score of discourse segmentation reaches 89.1%. When parsing discourses with no more than 6 relations included, the labeling F-score can achieve 63%.
For languages such as English, several constituent-to-dependency conversion schemes are pro-posed to construct corpora for dependency parsing. It is hard to determine which scheme is better because they reflect different views of dependency analysis. We usually obtain dependen-cy parsers of different schemes by training with the specific corpus separately. It neglects the correlations between these schemes, which can potentially benefit the parsers. In this paper, we study how these correlations influence final dependency parsing performances, by proposing a joint model which can make full use of the correlations between heterogeneous dependencies, and finally we can answer the following question: parsing heterogeneous dependencies jointly or separately, which is better? We conduct experiments with two different schemes on the Penn Treebank and the Chinese Penn Treebank respectively, arriving at the same conclusion that joint-ly parsing heterogeneous dependencies can give improved performances for both schemes over the individual models.
Part-of-speech (POS) taggers can be quite accurate, but for practical use, accuracy often has to be sacrificed for speed. For example, the maintainers of the Stanford tagger (Toutanova et al., 2003; Manning, 2011) recommend tagging with a model whose per tag error rate is 17% higher, relatively, than their most accurate model, to gain a factor of 10 or more in speed. In this paper, we treat POS tagging as a single-token independent multiclass classification task. We show that by using a rich feature set we can obtain high tagging accuracy within this framework, and by employing some novel feature-weight-combination and hypothesis-pruning techniques we can also get very fast tagging with this model. A prototype tagger implemented in Perl is tested and found to be at least 8 times faster than any publicly available tagger reported to have comparable accuracy on the standard Penn Treebank Wall Street Journal test set.
What factors contribute to subjective experiences of familiarity, and are these subject to unconscious selection? We investigated the circumstances under which judgments of familiarity are sensitive to task-irrelevant sources using the artificial grammar learning paradigm, a task known to be heavily reliant on familiarity-based responding. In 2 experiments, we manipulated ‘free-floating feelings of familiarity’ by subliminally priming participants with either a subjectively familiar stimulus (their surname) or unfamiliar stimulus (a random letter string). In Experiment 1, after training on an artificial grammar, participants were required to rate the familiarity of a new set of grammar strings where the subliminal priming manipulation preceded each rating. Under these instructions the manipulation significantly altered ratings of familiarity. In Experiment 2, the training, the request for familiarity ratings, and the subliminal manipulation were all unchanged. In addition, however, participants were informed about the presence of rules dictating the structure of the training strings and were required to judge both whether each test-string conformed to those rules and to report the basis for their judgment. This broader decision context eliminated the effect of subliminal primes on ratings of familiarity even when participants’ reported basis for their judgments revealed no conscious knowledge of the rule structure. These results demonstrate that unconscious sources of familiarity can be selected or excluded according to conscious task contexts. The findings are incompatible with theories that equate familiarity with automaticity and those that state people must always be aware of the structural antecedents of metacognition.
The global spread of English and the advent of a need for English as an International Language has become one of the hotly-debated issues in recent years. This owes much to the fact that English speakers today are more likely to be non-native speakers of English than native speakers, and most likely to use English in communication with other non-native speakers of English than native speakers. A significant number of scholars (e.g., Honna, 2003; Widdowson, 2003) even believe that English is no longer the sole property of its native speakers. Nevertheless, majority of English language teaching coursebooks are still being published by major Anglo-American publishers and are based on the linguistic norms and cultures of native English speaking countries, mainly the USA and the UK. Inevitably, criticism regarding an accurate presentation of cultural information and images about a variety of norms and cultures beyond the Anglo-Saxon and European world has risen. In fact, the English presented in these coursebooks has been seen as mainly representing the linguistic norms and culture of its native speakers, thereby offering ‘English of Specific Cultures’. The current discussions on the English language teaching and culture axis, however, make possible an understanding of an English language that has become first international and then global, thereby creating possibilities of portrayal of linguistic norms and cultures of Outer and Expanding circle countries especially through ELT coursebooks. Commissioned as such, then, English can be regarded as a language through which access to Englishes and cultures of the world accompanies its pedagogy, hence ‘ English for Specific Cultures’ (Yano, 2009). Discussing at length the role of English as an International Language and its cultural implications, this article investigates the varieties of Englishes in a series of EIL-based coursebooks, inquiring whether they are based on English of Specific Cultures or English for Specific Cultures.
We present a new dependency parsing method for Korean applying cross-lingual transfer learning and domain adaptation techniques. Unlike existing transfer learning methods relying on aligned corpora or bilingual lexicons, we propose a feature transfer learning method with minimal supervision, which adapts an existing parser to the target language by transferring the features for the source language to the target language. Specifically, we utilize the Triplet/Quadruplet Model, a hybrid parsing algorithm for Japanese, and apply a delexicalized feature transfer for Korean. Experiments with Penn Korean Treebank show that even using only the transferred features from Japanese achieves a high accuracy (81.6%) for Korean dependency parsing. Further improvements were obtained when a small annotated Korean corpus was combined with the Japanese training corpus, confirming that efficient crosslingual transfer learning can be achieved without expensive linguistic resources.
Languages that have no explicit word de-limiters often have to be segmented for sta-tistical machine translation (SMT). This is commonly performed by automated seg-menters trained on manually annotated corpora. However, the word segmentation (WS) schemes of these annotated corpora are handcrafted for general usage, and may not be suitable for SMT. An analysis was performed to test this hypothesis us-ing a manually annotated word alignment (WA) corpus for Chinese-English SMT. An analysis revealed that 74.60 % of the sentences in the WA corpus if segmented using an automated segmenter trained on the Penn Chinese Treebank (CTB) will contain conflicts with the gold WA an-notations. We formulated an approach based on word splitting with reference to the annotated WA to alleviate these con-flicts. Experimental results show that the refined WS reduced word alignment error rate by 6.82 % and achieved the highest BLEU improvement (0.63 on average) on the Chinese-English open machine trans-lation (OpenMT) corpora compared to re-lated work. 1
The conceptions of a linguistic norm for non-linguists form the focus of this article. These conceptions manifest themselves as mental values in the speakers’ minds and can be viewed as implicit reference values of linguistic judgements. There will hereby be an attempt to reconstruct the conceptual contours of people without any academic background in linguistics and to illustrate which criteria play a role in the assessments given and which structural domains can even be assessed. With respect to the method employed here, the study builds on the qualitative content analysis used for extracting and interpreting data. The result of which forms a categorical system that has been constructed deductively and rechecked inductively by the material. The system of categories constructed, which is empirically based on 56 qualitative interviews, also forms the data’s interpretative framework. With the aid of the acquired results, it is shown that non-linguists certainly have a clear picture of what constitutes a good language or how a good language should be; it is closely oriented on written language, invariant, understandable, and has a high communicative scope. In this way, a complex and multilayered conception of linguistic norms can be established. The contours of which will be sketched in this article.
In this article, we propose the first work that investigates the feasibility of Arabic discourse segmentation into elementary discourse units within the segmented discourse representation theory framework. We first describe our annotation scheme that defines a set of principles to guide the segmentation process. Two corpora have been annotated according to this scheme: elementary school textbooks and newspaper documents extracted from the syntactically annotated Arabic Treebank. Then, we propose a multiclass supervised learning approach that predicts nested units. Our approach uses a combination of punctuation, morphological, lexical, and shallow syntactic features. We investigate how each feature contributes to the learning process. We show that an extensive morphological analysis is crucial to achieve good results in both corpora. In addition, we show that adding chunks does not boost the performance of our system.
We investigate how the granularity of POS tags influences POS tagging, and furthermore, how POS tagging performance relates to parsing results.For this, we use the standard "pipeline" approach, in which a parser builds its output on previously tagged input.The experiments are performed on two German treebanks, using three POS tagsets of different granularity, and six different POS taggers, together with the Berkeley parser.Our findings show that less granularity of the POS tagset leads to better tagging results.However, both too coarse-grained and too fine-grained distinctions on POS level decrease parsing performance.
The aim of this article is to measure the indexes of productivity of the prefix ful - and the suffix - ful in Old English adjective formation. This analysis is based on Baayen’s framework, which comprises different measures on productivity. The major sources of the analysis are The Dictionary of Old English Corpus and the lexical database of Old English Nerthus. This study of productivity allows for a diachronic perspective on the evolution of these affixes from the Old English period to the present. The main conclusion drawn from this analysis is that the suffix -ful is more productive than its prefixal counterpart, which implies that more productive patterns are still maintained in Present-day English in contradistinction to the less productive ones.
We investigate the usefulness of syntactic knowledge in estimating the quality of English-French translations. We find that dependency and constituency tree kernels perform well but the error rate can be further reduced when these are combined with hand-crafted syntactic features. Both types of syntactic features provide information which is complementary to tried-and-tested nonsyntactic features. We then compare source and target syntax and find that the use of parse trees of machine translated sentences does not affect the performance of quality estimation nor does the intrinsic accuracy of the parser itself. However, the relatively flat structure of the French Treebank does appear to have an adverse effect, and this is significantly improved by simple transformations of the French trees. Finally, we provide further evidence of the usefulness of these transformations by applying them in a separate task ‐ parser accuracy prediction.
The architecture of writing systems metaphor has special relevance for understanding the structural nature of the Japanese writing system, and, more specifically, for appreciating how the 2,136 kanji of the 常用漢字表 /jō-yō-kan-ji-hyō/* ‘List of characters for general use’ function as the core building blocks in the orthographic representation of a considerable proportion of the Japanese lexicon. In seeking to illuminate the multiple layers of internal structure within Japanese kanji, the Japanese lexicon, and the Japanese writing system, the paper draws on insights and observations gained from an ongoing project to construct a large-scale Japanese lexical database system. Reflecting structural distinctions within the database, the paper consists of three main sections addressing the different structural levels of kanji components, jōyō kanji, and the lexicon. Keywords: Japanese writing system; building blocks; jōyō kanji; components; orthographic structure; database
In this paper we present several approaches towards constructing joint ensemble models for mor-phosyntactic tagging and dependency parsing for a morphologically rich language – Bulgarian. In our experiments we use state-of-the-art taggers and dependency parsers to obtain an extended version of the treebank for Bulgarian, BulTreeBank, which, in addition to the standard CoNLL fields, contains predicted morphosyntactic tags and dependency arcs for each word. In order to select the most suitable tag and arc from the proposed ones, we use several ensemble techniques, the result of which is a valid dependency tree. Most of these approaches show improvement over the results achieved individually by the tools for tagging and parsing. 1
This paper reviews the understanding divergence of the termsentence patternof Chinese grammar scholars;from the perspective of Chinese information processing, it analyses the lack of sentence pattern structure in current syntactic parsing and treebank construction in this field; and gives a recent review of formalization research of Li Jinxi's grammar system, indicating its strengths and still shortcomings on sentence pattern structure; it uses Li Jinxi's diagrammatic parsing method as a prototype design of a new type of diagrammatic parsing method of Chinese syntactic structure, specifically including a diagrammatic representation of the syntactic structure and structured XML storage format.
Inviting Citizen Designers to Design Learning Management System (LMS) Interfaces for Student Agency in a Digital Cross-Cultural Contact Zone assesses how FYC students from periphery cultural and linguistic backgrounds perceive Blackboard Learn and other learning management system (LMS) interfaces. The report of an empirical study shows that the current LMS design does not provide writing students in general and writing students from periphery cultural and linguistic backgrounds in particular an opportunity of a higher-level interactivity with the LMS. The current design neither includes periphery students' cultural and linguistic norms and values, nor does it allow them to affect the existing design through their design activities. These LMSs are currently constraining users from higher-level interactions. As a result, writing students have to act as the LMS ask them to do, and they remain passive in these platforms. Based on the web usability test responses, this study proposes to invite Citizen Designers, writing students from periphery cultural and linguistic backgrounds, to design LMS interfaces to enhance user activities and transform them into cross-cultural platforms. This study analyzes interface designs by Citizen Designers to see how designers acquire their agency in a cross-cultural digital contact zone. This study concludes that Citizen Designers' participation in interface design helps them create favorable electronic environments that help them acquire their agency and enhance their (digital) writings and researches.
Parsing the Arabic language is a difficult task given the specificities of this language and given the scarcity of digital resources (grammars and annotated corpora). In this paper, we suggest a method for Arabic parsing based on supervised machine learning. We used the SVMs algorithm to select the syntactic labels of the sentence. Furthermore, we evaluated our parser following the cross validation method by using the Penn Arabic Treebank. The obtained results are very encouraging.
This article summarizes the impact of inter-generational cooperation on the quality of life of elderly Alzheimer’s sufferers. The study is a continuing, two-year intervention and reports the results of the first year. It consists of an intervention and a control group of eight and six sufferers, respectively, who have been diagnosed with Alzheimer's disease. Both groups attend day care services. The intervention group participates in the inter-generational program with children, while the control group does not. On the Philadelphia Geriatric Center Affect Rating Scale, three items have been proved statistically significant. Pleasure, Interest, and Contentment have increased with inter-generational cooperation. The magnitude of the change was not so remarkable as to influence QOL-AD at home. However, the present results may imply a reduction on the burden of the day care service staff and family carers. Another advantage may be in the educating of the children’s parents, whose understanding of dementia was poor.
This journal article draws a distinction between the split and unified functions obtaining in the formation of Old English nouns and adjectives. The starting point of the discussion is an enlarged inventory of lexical functions that draw on Meaning-Text Theory and structural-functional grammars and explain the change of meaning caused by prefixation and suffixation in Old English. The extended inventory of lexical functions consists of 33 functions and has been applied to ca. 7,500 affixed nouns and adjectives extracted from the lexical database Nerthus (www.nerthusproject.com). The distinction between split and unified functions, in such a way that the former can be realized by both prefixes and suffixes and the latter by either prefixation or suffixation, allows for some generalizations. Firstly, the analysis proves that there are more functions involved in prefixation than in suffixation. Secondly, prefixation is meaning oriented while suffixation is class oriented.
Syntactic parsing is a fundamental problem of natural language processing, and statistical syntactic parsing based on treebank gradually becomes the mainstream techniques of modern syntactic parsing following the building of large scale annotated treebanks. Firstly, the main treebanks and the main methods to measure syntactic parsing system performances were described. Secondly, the main statistical syntactic parsing models and the current researches on the Chinese syntactic parsing were presented and analyzed. Finally, the problems and the future study trends of statistical syntactic parsing were discussed and summarized. Is is pointed that Chinese syntax analysis methods are not suitable for the features of Chinese, and they do not effectively characterize the essential features of Chinese, thus the performances of syntactic parsing of Chinese are far below the performances of English. To integrate semantic information in syntactic parsing and to establish a joint syntactic and semantic statistical parsing model based on. semantic analysis will be a important study direction of syntactic parsing.
Official releases of the PROIEL treebank of ancient Indo-European languages
Official releases of the PROIEL treebank of ancient Indo-European languages
OBJECTIVE: The neuropeptide oxytocin is implicated in social processing, and recent research has begun to explore how gender relates to the reported effects. This study examined the effects of oxytocin on social affective perception and learning. METHODS: Forty-seven male and female participants made judgments of faces during two different tasks, after being randomized to either double-blinded intranasal oxytocin or placebo. In the first task, "unseen" affective stimuli were presented in a continuous flash suppression paradigm, and participants evaluated faces paired with these stimuli on dimensions of competence, trustworthiness, and warmth. In the second task, participants learned affective associations between neutral faces and affective acts through a gossip learning procedure and later made affective ratings of the faces. RESULTS: In both tasks, we found that gender moderated the effect of oxytocin, such that male participants in the oxytocin condition rated faces more negatively, compared with placebo. The opposite pattern of findings emerged for female participants: they rated faces more positively in the oxytocin condition, compared with placebo. CONCLUSIONS: These findings contribute to a small but growing body of research demonstrating differential effects of oxytocin in men and women.
WordNet semantic classes to improve dependency parsing. We study the effect of semantic classes in three dependency parsers, using two types of constituencyto-dependency conversions of the English Penn Treebank. Overall, we can say that the improvements are small and not significant using automatic POS tags, contrary to previously published results using gold POS tags In addition, we explore parser combinations, showing that the semantically enhanced parsers yield a small significant gain only on the more semantically oriented LTH treebank conversion.
The objective of this paper is to provide an overview of the CDT annotation design with special emphasis on the modelling of the interface between the syntactic level and two other linguistic levels, viz. morphology and discourse. In connection with the description of NP annotation we present the fundamentals of how CDT is marked up with semantic relations in accordance with the dependency principles governing the annotation on the other levels of CDT. Specifically, focus will be on how Generative Lexicon (GL) theory has been incorporated into the unitary theoretical dependency framework of CDT. An annotation scheme for lexical semantics has been designed so as to account for the lexico-semantic structure of complex NPs, and the four GL qualia also appear in some of the CDT discourse relation labels as a description of parallel semantic relations at this level.
Studies comparing memory and future event simulation find that future events are more positive, and more often depend on life script events (e.g., culturally normative landmark events) than past events. Previous research does not address the link between this positivity bias and the life stage of college-age participants or their reliance on these scripted events. To examine this positivity bias, narratives of past and anticipated future events were elicited from participants aged 18-74 years, and were examined for reliance on the life script and valence ratings. Results showed that, across age groups, future events were rated as more positive than past events, and that life script events were common in the distant future. Notably, whereas younger adult age groups wrote primarily about their own life script events, older participants more commonly wrote about attending the life script events of significant others, such as children and grandchildren. These findings suggest that simulated future events play a valuable role in self-enhancement across the lifespan. Furthermore, the life script can be viewed as a useful search mechanism when one is missing the episodic details that are more available in memories; however, it is not the source of positivity bias for future events.
We present a user-centered approach for defining the dependency syntactic specification for a treebank. We show that by collecting information on syntactic interpretations from the future users of the treebank, we can model so far dependency-syntactically undefined syntactic structures in a way that corresponds to the users’ intuition. By consulting the users at the grammar definition phase we aim at better usage of the treebank in the future. We focus on two complex syntactic phenomena: elliptical comparative clauses and participial NPs or NPs with a verb-derived noun as their head. We show how the phenomena can be interpreted in several ways and ask for the users’ intuitive way of modeling them. The results aid in constructing the syntactic specification for the treebank.
Social media applications such as Twitter provide a powerful medium through which users can communicate their observations with friends and with the world at large. We have witnessed live reporting of many events, from soccer games in Johannesburg to revolutions in Cairo and Tunis, and these reports have in many ways rivaled the content provided by the official media. Tapping into this valuable resource is a challenge, due to the heterogeneity and noise inherent in realtime text, diversity of languages, and fast-evolving linguistic norms. In this paper we seek to analyze a tweet stream to automatically discover points in time when an important event happens, and to classify such events based on the type of the sentiments they evoke, using only non-textual features of the tweeting pattern. This results not only in a robust way of analyzing tweet streams independent of the languages used; it also provides insights about how users behave on social media websites. For example, we observe that users often react to an exciting external event by decreasing the volume of communication with other users. We explain this effect through a model of how users switch between producing information or sentiments and sharing others’ news or sentiments. We develop and evaluate our models and algorithms using several Twitter data sets, focusing in particular on the tweets sent during the soccer World Cup of 2010. This data set has the feature that the underlying ground truth is welldefined and known whereby goals serve as events.
Abstract This study was conducted to understand the relationship between familiarity and cross‐cultural acceptance for an ethnic sweet treat ( Y ackwa; K orean traditional cookie) by K orean, J apanese and F rench consumers. Descriptive analysis and consumer testing were performed on six Y ackwa samples. Overall, the samples received favorable responses from the foreign consumers. K orean consumers liked samples with a soft and cohesive texture, whereas J apanese and F rench consumers liked flaky and crispy texture. French consumers rated stronger sweetness to be more appropriate for Y ackwa compared to K orean and J apanese consumers. Texture liking was strongly correlated with familiarity rating in all three countries, indicating that the consumers' previous experience with similar products might affect their preference for certain textural attributes. Familiarity was correlated with all hedonic ratings by K orean consumers, who are most familiar with Y ackwa, but with overall and texture liking by J apanese consumers and flavor and texture liking by French consumers. These results suggest that familiarity partly contributes to a foreign consumers' hedonic rating. Practical Applications Globalization and cultural diversity have increased interest in ethnic foods. This trend is motivating food industries to expand into the ethnic food market sector. In this study, the sensory attributes and the cross‐cultural acceptability of Y ackwa ( K orean traditional cookie) were evaluated and the potential role of familiarity in determining consumer acceptance was measured. The outcome of this study will help food exporters, R&D scientists and food marketers in ethnic food market to optimize an ethnic food for other cultural communities by educating them to consider familiarity as an important factor for product development and promotion.
BACKGROUND: Depression is frequently characterized by patterns of inflexible, maladaptive, and ruminative thinking styles, which are thought to result from a combination of decreased attentional control, decreased executive functioning, and increased negative affect. Cognitive Control Training (CCT) uses computer-based behavioral exercises with the aim of strengthening cognitive and emotional functions. A previous study found that severely depressed participants who received CCT exhibited reduced negative affect and rumination as well as improved concentration. AIMS: The present study aimed to extend this line of research by employing a more stringent control group and testing the efficacy of three sessions of CCT over a 2-week period in a community population with depressed mood. METHOD: Forty-eight participants with high Beck Depression Inventory (BDI-II) scores were randomized to CCT or a comparison condition (Peripheral Vision Training; PVT). RESULTS: Significant large effect sizes favoring CCT over PVT were found on the BDI-II (d = 0.73, p <.05) indicating CCT was effective in reducing negative mood. Additionally, correlations showed significant relationships between CCT performance (indicating ability to focus attention on CCT) and state affect ratings. CONCLUSIONS: Our results suggest that CCT is effective in altering depressed mood, although it may be specific to select mood dimensions.
We present a framework for identifying the most representative sentence patterns from semantically and syntactically-annotated corpora via a Semantic Frame Generation (SFG). One of the difficulties to find out similar concepts from a text is because of the variations in linguistic expressions. SFG uses linguistic units as backbones to generate the most prominent patterns from various Chinese DE phrases.
Recursive neural models have achieved promising results in many natural language processing tasks. The main difference among these models lies in the composition function, i.e., how to obtain the vector representation for a phrase or sentence using the representations of words it contains. This paper introduces a novel Adaptive Multi-Compositionality (AdaMC) layer to recursive neural models. The basic idea is to use more than one composition functions and adaptively select them depending on the input vectors. We present a general framework to model each semantic composition as a distribution over these composition functions. The composition functions and parameters used for adaptive selection are learned jointly from data. We integrate AdaMC into existing recursive neural models and conduct extensive experiments on the Stanford Sentiment Treebank. The results illustrate that AdaMC significantly outperforms state-of-the-art sentiment classification methods. It helps push the best accuracy of sentence-level negative/positive classification from 85.4% up to 88.5%.