Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
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.
In our evolving world, new technologies and practices are frequently introduced to society and assimilated into daily life. People often form concerns about how these new technologies, and other types of change, affect public health and the surrounding environment. This paper aims to form a better understanding of Modern Health Worries (MHW). Two studies were conducted: one investigating personality correlates of the MHW scale, and a second examining the covariation of the MHW scale with participants’ valence and arousal ratings of images of MHWs selected in terms categories presented in the literature. Undergraduate students at Syracuse University (n=143) took part in the first study where they completed a series of online questionnaires measuring personality factors and MHWs. Results of the first study indicated that openness to experience moderates the relationship between MHW and neuroticism. The second study was also comprised of undergraduate students at Syracuse University (n=27). In this exploratory study, participants completed the same questionnaires, but also rated images corresponding to specific MHWs based on valence and arousal. Findings suggest that as an individual reports higher levels of MHW, he or she reports experiencing more negative valence from images corresponding to MHWs. In addition, the second study found that participants rated images of environmental pollution and water contamination with the most negative valence. Future research in the area of MHWs should take into account these associations. In addition, the specific images with high ratings for negative valence should be used in future cue reactivity studies about MHWs. Key Words: Modern Health Worry, Personality, Openness to experience, Neuroticism, Valence, and Agreeableness
Official releases of the PROIEL treebank of ancient Indo-European languages
Dependency parsing has gained more and more interest in natural language processing in recent years due to its simplicity and general applicability for diverse languages. The international conference of computational natural language learning (CoNLL) has organized shared tasks on multilingual dependency parsing successively from 2006 to 2009, which leads to extensive progress on dependency parsing in both theoretical and practical perspectives. Meanwhile, dependency parsing has been successfully applied to machine translation, question answering, text mining, etc. To date, research on dependency parsing mainly focuses on data-driven supervised approaches and results show that the supervised models can achieve reasonable performance on in-domain texts for a variety of languages when manually labeled data is provided. However, relatively less effort is devoted to parsing out-domain texts and resource-poor languages, and few successful techniques are bought up for such scenario. This tutorial will cover all these research topics of dependency parsing and is composed of four major parts. Especially, we will survey the present progress of semi-supervised dependency parsing, web data parsing, and multilingual text parsing, and show some directions for future work. In the first part, we will introduce the fundamentals and supervised approaches for dependency parsing. The fundamentals include examples of dependency trees, annotated treebanks, evaluation metrics, and comparisons with other syntactic formulations like constituent parsing. Then we will introduce a few mainstream supervised approaches, i.e., transition-based, graph-based, easy-first, constituent-based dependency parsing. These approaches study dependency parsing from different perspectives, and achieve comparable and state-of-the-art performance for a wide range of languages. Then we will move to the hybrid models that combine the advantages of the above approaches. We will also introduce recent work on efficient parsing techniques, joint lexical analysis and dependency parsing, multiple treebank exploitation, etc. In the second part, we will survey the work on semi-supervised dependency parsing techniques. Such work aims to explore unlabeled data so that the parser can achieve higher performance. This tutorial will present several successful techniques that utilize information from different levels: whole tree level, partial tree level, and lexical level. We will discuss the advantages and limitations of these existing techniques. In the third part, we will survey the work on dependency parsing techniques for domain adaptation and web data. To advance research on out-domain parsing, researchers have organized two shared tasks, i.e., the CoNLL 2007 shared task and the shared task of syntactic analysis of non-canonical languages (SANCL 2012). Both two shared tasks attracted many participants. These participants tried different techniques to adapt the parser trained on WSJ texts to out-domain texts with the help of large-scale unlabeled data. Especially, we will present a brief survey on text normalization, which is proven to be very useful for parsing web data. In the fourth part, we will introduce the recent work on exploiting multilingual texts for dependency parsing, which falls into two lines of research. The first line is to improve supervised dependency parser with multilingual texts. The intuition behind is that ambiguities in the target language may be unambiguous in the source language. The other line is multilingual transfer learning which aims to project the syntactic knowledge from the source language to the target language.
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.
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.
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.
We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license.
Methylphenidate mainly enhances dopamine neurotransmission whereas 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") mainly enhances serotonin neurotransmission. However, both drugs also induce a weaker increase of cerebral noradrenaline exerting sympathomimetic properties. Dopaminergic psychostimulants are reported to increase sexual drive, while serotonergic drugs typically impair sexual arousal and functions. Additionally, serotonin has also been shown to modulate cognitive perception of romantic relationships. Whether methylphenidate or MDMA alter sexual arousal or cognitive appraisal of intimate relationships is not known. Thus, we evaluated effects of methylphenidate (40 mg) and MDMA (75 mg) on subjective sexual arousal by viewing erotic pictures and on perception of romantic relationships of unknown couples in a double-blind, randomized, placebo-controlled, crossover study in 30 healthy adults. Methylphenidate, but not MDMA, increased ratings of sexual arousal for explicit sexual stimuli. The participants also sought to increase the presentation time of implicit sexual stimuli by button press after methylphenidate treatment compared with placebo. Plasma levels of testosterone, estrogen, and progesterone were not associated with sexual arousal ratings. Neither MDMA nor methylphenidate altered appraisal of romantic relationships of others. The findings indicate that pharmacological stimulation of dopaminergic but not of serotonergic neurotransmission enhances sexual drive. Whether sexual perception is altered in subjects misusing methylphenidate e.g., for cognitive enhancement or as treatment for attention deficit hyperactivity disorder is of high interest and warrants further investigation.
It is well established that categorising the emotional content of facial expressions may differ depending on contextual information. Whether this malleability is observed in the auditory domain and in genuine emotion expressions is poorly explored. We examined the perception of authentic laughter and crying in the context of happy, neutral and sad facial expressions. Participants rated the vocalisations on separate unipolar scales of happiness and sadness and on arousal. Although they were instructed to focus exclusively on the vocalisations, consistent context effects were found: For both laughter and crying, emotion judgements were shifted towards the information expressed by the face. These modulations were independent of response latencies and were larger for more emotionally ambiguous vocalisations. No effects of context were found for arousal ratings. These findings suggest that the automatic encoding of contextual information during emotion perception generalises across modalities, to purely non-verbal vocalisations, and is not confined to acted expressions.
Measuring the similarity of words is important in accurately representing and comparing documents, and thus improves the results of many natural language processing (NLP) tasks. The NLP community has proposed various measurements based on WordNet, a lexical database that contains relationships between many pairs of words. Recently, a number of techniques have been proposed to address software engineering issues such as code search and fault localization that require understanding natural language documents, and a measure of word similarity could improve their results. However, WordNet only contains information about words senses in general-purpose conversation, which often differ from word senses in a software-engineering context, and the software-specific word similarity resources that have been developed rely on data sources containing only a limited range of words and word uses.
BACKGROUND: Patients with schizophrenia often experience problems regulating their emotions. Non-affected relatives show similar difficulties, although to a lesser extent, and the neural basis of such difficulties remains to be elucidated. In the current paper we investigated whether schizophrenia patients, non-affected siblings and healthy controls (HC) exhibit differences in brain activation during emotion regulation. METHODS: All subjects (n = 20 per group) performed an emotion regulation task while they were in an fMRI scanner. The task contained two experimental conditions for the down-regulation of emotions (reappraise and suppress), in which IAPS pictures were used to generate a negative affect. We also assessed whether the groups differed in emotion regulation strategies used in daily life by means of the emotion regulation questionnaire (ERQ). RESULTS: Though the overall negative affect was higher for patients as well as for siblings compared to HC for all conditions, all groups reported decreased negative affect after both regulation conditions. Nonetheless, neuroimaging results showed hypoactivation relative to HC in VLPFC, insula, middle temporal gyrus, caudate and thalamus for patients when reappraising negative pictures. In siblings, the same pattern was evident as in patients, but only in cortical areas. CONCLUSIONS: Given that all groups performed similarly on the emotion regulation task, but differed in overall negative affect ratings and brain activation, our findings suggest reduced levels of emotion regulation processing in neural circuits in patients with schizophrenia. Notably, this also holds for siblings, albeit to a lesser extent, indicating that it may be part and parcel of a vulnerability for psychosis.
After a period when the focus was essentially on mental architecture, the cognitive sciences are increasingly integrating the social dimension. The rise of a cognitive sociolinguistics is part of this trend. The article argues that this process requires a re-evaluation of some entrenched positions in linguistics: those that see linguistic norms as antithetical to a descriptive and variational linguistics. Once such a re-evaluation has taken place, however, the social recontextualization of cognition will enable linguistics (including sociolinguistics as an integral part), to eliminate the cracks in the foundations that were the result of suppressing the sociocultural underpinnings of linguistic facts. Structuralism, cognitivism and social constructionism introduced new and necessary distinctions, but in their strong forms they all turned into unnecessary divides. The article tries to show that an evolutionary account can reintegrate the opposed fragments into a whole picture that puts each of them in their ‘ecological position’ with respect to each other. Empirical usage facts should be seen in the context of operational norms in relation to which actual linguistic choices represent adaptations. Variational patterns should be seen in the context of structural categories without which there would be only ‘differences’ rather than variation. And emergence, individual choice, and flux should be seen in the context of the individual’s dependence on lineages of community practice sustained by collective norms.
International audience
Comunicació presentada al 9th International Conference on Language Resources and Evaluation (LREC'14), celebrat del 26 al 31 de maig de 2014 a Reykjavík, Islàndia.
OBJECTIVE: This study investigated how lexical effects account for word recognition in monolinguals versus bilinguals. DESIGN: Listener-specific error rate and familiarity rating of 200 NU-6 words were obtained. Lexical data (normative familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for these words were obtained from the Hoosier mental lexicon. STUDY SAMPLE: Participants included 10 monolinguals and three groups of 10 bilinguals differing mainly in age of acquisition and length of schooling/working in English. RESULTS: Lexical effects were minimal for monolinguals' word recognition. Listener-specific familiarity rating correlated to error rate better than the Hoosier normative rating. Frequency of occurrence was the most significant lexical variable in accounting for bilinguals' measures and its effect was the greatest on bilinguals foreign born and educated. Age of English acquisition tended to affect familiarity rating, whereas length of schooling/working in English tended to affect error rate. CONCLUSIONS: Frequency of word occurrence significantly affects bilinguals' familiarity rating and error rate of the NU-6 words. Listener-specific familiarity rating should be obtained to best predict error rate on the test.
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%.
This paper presents the first results on parsing the Penn Parsed Corpus of Modern British English (PPCMBE), a millionword historical treebank with an annotation style similar to that of the Penn Treebank (PTB). We describe key features of the PPCMBE annotation style that differ from the PTB, and present some experiments with tree transformations to better compare the results to the PTB. First steps in parser analysis focus on problematic structures created by the parser.
The article focuses on the hypothesis that the structural complexity of languages is variable and historically changeable. By means of a quantitative statistical analysis of naturalistic corpus data, the question is raised as to what role language contact and adult second language acquisition play in the simplification and complexification of language varieties. The results confirm that there is a significant correlation between intensity of contact and linguistic complexity, while at the same time showing that there is a need to consider other social factors, and, in particular, the attitude of a speech community toward linguistic norms. *
The extinction of conditioned fear depends on an efficient interplay between the amygdala and the medial prefrontal cortex (mPFC). In rats, high-frequency electrical mPFC stimulation has been shown to improve extinction by means of a reduction of amygdala activity. However, so far it is unclear whether stimulation of homologues regions in humans might have similar beneficial effects. Healthy volunteers received one session of either active or sham repetitive transcranial magnetic stimulation (rTMS) covering the mPFC while undergoing a 2-day fear conditioning and extinction paradigm. Repetitive TMS was applied offline after fear acquisition in which one of two faces (CS+ but not CS-) was associated with an aversive scream (UCS). Immediate extinction learning (day 1) and extinction recall (day 2) were conducted without UCS delivery. Conditioned responses (CR) were assessed in a multimodal approach using fear-potentiated startle (FPS), skin conductance responses (SCR), functional near-infrared spectroscopy (fNIRS), and self-report scales. Consistent with the hypothesis of a modulated processing of conditioned fear after high-frequency rTMS, the active group showed a reduced CS+/CS- discrimination during extinction learning as evident in FPS as well as in SCR and arousal ratings. FPS responses to CS+ further showed a linear decrement throughout both extinction sessions. This study describes the first experimental approach of influencing conditioned fear by using rTMS and can thus be a basis for future studies investigating a complementation of mPFC stimulation to cognitive behavioral therapy (CBT).
We present an algorithm and implementation for extracting recurring fragments from treebanks. Using a tree-kernel method the largest common fragments are extracted from each pair of trees. The algorithm presented achieves a thirty-fold speedup over the previously available method on the Wall Street Journal dataset. It is also more general, in that it supports trees with discontinuous constituents. The resulting fragments can be used as a tree-substitution grammar or in classification problems such as authorship attribution and other stylometry tasks.
Dictionaries are designed as huge texts made up of a collection of much smaller texts, i.e. lexicographic articles. To put it differently, dictionaries are two-dimensional textual models of natural language lexicons. Lexicographers, however, are well aware of the fact that their task is to account for a truly multidimensional entity: a gigantic graph of lexical units connected by various paradigmatic and syntagmatic relations. The most significant advance that computer science will bring to the future of lexicography is therefore not the ability to better store, search and manipulate textual lexicographic data; it will be to allow lexicographers to bypass the text as a formal representation of lexicons and to directly work on lexical networks. Such networks are more suitable to the lexicographic endeavour because they are better formal metaphors of the “natural” structure we are trying to account for. This paper presents lexical systems as graph models of lexicons and introduces the corresponding lexicography of virtual dictionaries. It is based on extensive lexicographic work that is being conducted on the French Lexical Network, a lexical database built according to theoretical and methodological principles borrowed from Explanatory Combinatorial Lexicology.
Studies in classifying affect from vocal cues have produced exceptional within-corpus results, especially for arousal (activation or stress); yet cross-corpora affect recognition has only recently garnered attention. An essential requirement of many behavioral studies is affect scoring that generalizes across different social contexts and data conditions. We present a robust, unsupervised (rule-based) method for providing a scale-continuous, bounded arousal rating operating on the vocal signal. The method incorporates just three knowledge-inspired features chosen based on empirical and theoretical evidence. It constructs a speaker's baseline model for each feature separately, and then computes single-feature arousal scores. Lastly, it advantageously fuses the single-feature arousal scores into a final rating without knowledge of the true affect. The baseline data is preferably labeled as neutral, but some initial evidence is provided to suggest that no labeled data is required in certain cases. The proposed method is compared to a state-of-the-art supervised technique which employs a high-dimensional feature set. The proposed framework achieves highly-competitive performance with additional benefits. The measure is interpretable, scale-continuous as opposed to discrete, and can operate without any affective labeling. An accompanying Matlab tool is made available with the paper.
In pursuing machine understanding of human language, highly accurate syntactic analysis is a crucial step. In this work, we focus on dependency grammar, which models syntax by encoding transparent predicate-argument structures. Recent advances in dependency parsing have shown that employing higher-order subtree structures in graph-based parsers can substantially improve the parsing accuracy. However, the inefficiency of this approach increases with the order of the subtrees. This work explores a new reranking approach for dependency parsing that can utilize complex subtree representations by applying efficient subtree selection methods. We demonstrate the effectiveness of the approach in experiments conducted on the Penn Treebank and the Chinese Treebank. Our system achieves the best performance among known supervised systems evaluated on these datasets, improving the baseline accuracy from 91.88% to 93.42% for English, and from 87.39% to 89.25% for Chinese.
Social interaction deficits in drug users likely impede treatment, increase the burden of the affected families, and consequently contribute to the high costs for society associated with addiction. Despite its significance, the neural basis of altered social interaction in drug users is currently unknown. Therefore, we investigated basal social gaze behavior in cocaine users by applying behavioral, psychophysiological, and functional brain-imaging methods. In study I, 80 regular cocaine users and 63 healthy controls completed an interactive paradigm in which the participants' gaze was recorded by an eye-tracking device that controlled the gaze of an anthropomorphic virtual character. Valence ratings of different eye-contact conditions revealed that cocaine users show diminished emotional engagement in social interaction, which was also supported by reduced pupil responses. Study II investigated the neural underpinnings of changes in social reward processing observed in study I. Sixteen cocaine users and 16 controls completed a similar interaction paradigm as used in study I while undergoing functional magnetic resonance imaging. In response to social interaction, cocaine users displayed decreased activation of the medial orbitofrontal cortex, a key region of reward processing. Moreover, blunted activation of the medial orbitofrontal cortex was significantly correlated with a decreased social network size, reflecting problems in real-life social behavior because of reduced social reward. In conclusion, basic social interaction deficits in cocaine users as observed here may arise from altered social reward processing. Consequently, these results point to the importance of reinstatement of social reward in the treatment of stimulant addiction.
We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows information to flow not only bottom-up, as in traditional recursive neural networks, but also top-down. This is achieved by computing content as well as context representations for any constituent, and letting these rep-resentations interact. Experimental re-sults on the English section of the Uni-versal Dependency Treebank show that the infinite-order model achieves a per-plexity seven times lower than the tradi-tional third-order model using counting, and tends to choose more accurate parses in k-best lists. In addition, reranking with this model achieves state-of-the-art unla-belled attachment scores and unlabelled exact match scores. 1
David Walker’s famous 1829 Appeal to the Colored Citizens of the World expresses a puzzle at the very outset. What are we to make of the use of “Citizens” in the title given the denial of political rights to African Americans? This essay argues that the pamphlet relies on the cultural and linguistic norms associated with the term appeal in order to call into existence the political standing of black folks. Walker’s use of citizen does not need to rely on a recognitive legal relationship precisely because it is the practice of judging that illuminates one’s political, indeed, citizenly standing. Properly understood, the Appeal aspires to transform blacks and whites, and when it informs the prophetic dimension of the text, it tilts the entire pamphlet in a democratic direction. This is the political power of the pamphlet; it exemplifies the call-and-response logic of democratic self-governance.
We present a study of cross-lingual direct transfer parsing for the Irish language. Firstly we\ndiscuss mapping of the annotation scheme of the Irish Dependency Treebank to a universal dependency scheme. We explain our dependency label mapping choices and the structural changes\nrequired in the Irish Dependency Treebank. We then experiment with the universally annotated\ntreebanks of ten languages from four language family groups to assess which languages are the\nmost useful for cross-lingual parsing of Irish by using these treebanks to train delexicalised parsing models which are then applied to sentences from the Irish Dependency Treebank. The best\nresults are achieved when using Indonesian, a language from the Austronesian language family.
Negation words, such as no and not, play a fundamental role in modifying sentiment of textual expressions. We will refer to a negation word as the negator and the text span within the scope of the negator as the argument. Commonly used heuristics to estimate the sentiment of negated expressions rely simply on the sentiment of argument (and not on the negator or the argument itself). We use a sentiment treebank to show that these existing heuristics are poor estimators of sentiment. We then modify these heuristics to be dependent on the negators and show that this improves prediction. Next, we evaluate a recently proposed composition model (Socher et al., 2013) that relies on both the negator and the argument. This model learns the syntax and semantics of the negator's argument with a recursive neural network. We show that this approach performs better than those mentioned above. In addition, we explicitly incorporate the prior sentiment of the argument and observe that this information can help reduce fitting errors.
We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions. Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.
OBJECTIVE: In alcohol-dependent patients, alcohol cues evoke increased activation in mesolimbic brain areas, such as the nucleus accumbens and the amygdala. Moreover, patients show an alcohol approach bias, a tendency to more quickly approach than avoid alcohol cues. Cognitive bias modification training, which aims to retrain approach biases, has been shown to reduce alcohol craving and relapse rates. The authors investigated effects of this training on cue reactivity in alcohol-dependent patients. METHOD: In a double-blind randomized design, 32 abstinent alcohol-dependent patients received either bias modification training or sham training. Both trainings consisted of six sessions of the joystick approach-avoidance task; the bias modification training entailed pushing away 90% of alcohol cues and 10% of soft drink cues, whereas this ratio was 50/50 in the sham training. Alcohol cue reactivity was measured with functional MRI before and after training. RESULTS: Before training, alcohol cue-evoked activation was observed in the amygdala bilaterally, as well as in the right nucleus accumbens, although here it fell short of significance. Activation in the amygdala correlated with craving and arousal ratings of alcohol stimuli; correlations in the nucleus accumbens again fell short of significance. After training, the bias modification group showed greater reductions in cue-evoked activation in the amygdala bilaterally and in behavioral arousal ratings of alcohol pictures, compared with the sham training group. Decreases in right amygdala activity correlated with decreases in craving in the bias modification but not the sham training group. CONCLUSIONS: These findings provide evidence that cognitive bias modification affects alcohol cue-induced mesolimbic brain activity. Reductions in neural reactivity may be a key underlying mechanism of the therapeutic effectiveness of this training.
According to Tsinghua Chinese Treebank annotation methods, the authors extracted relation words and marked their categories. Then syntax, lexical and position features of automatic syntax tree with and without functional marker were extracted to recognize and classify relation words. Experiment results show that relative recognition accuracy is 95.7%, and relation words classification F1 is 77.2%.
In this paper, we analyze the impact of various dependency representations for various constructions on the general parsing accuracy and on the parsing accuracy of these constructions. We focus on the analysis of coordination constructions, complex predicates, and punctuation mark attachment. We use Latvian Treebank as a dataset, thus, providing insight for an inflective language with a rather free word order. Experiments with MaltParser, a transition-based parser, show clear difference in learnability of various representations for the considered constructions. Future work would include carrying out comparable experiments with a graph-based dependency parser like MSTParser.
In Dutch V-final clauses the verbs tend to form a cluster which cannot be split up by nonverbal \nmaterial. However, Haeseryn et al. (1997) as well as other studies on the phenomenon list several \ncases in which the verb cluster may be interrupted by \ncluster creepers. \nThe most common examples are constructions with separable verb particles, but examples with nouns, adjectives, and adverbs are attested as well. \nSince the majority of the data in previous studies is collected by introspection and elicitation, \nit is interesting to compare those findings to corpus data. The corpus analysis is based on data \nfrom two Dutch treebanks (CGN and LASSY), which allow to take into account regional and/or \nstylistic variation. This is an important aspect for the analysis, since cluster creeping is reported \nto be a typical property of spoken and regional variants of Dutch. \nThe goal of this corpus-based investigation is on the one hand to provide insight in the frequency \nof the phenomenon, and on the other hand to classify the types of cluster creepers. Besides the \nlinguistic analysis, methodological issues regarding the extraction of the relevant data from the \ntreebanks will be addressed as well.
The paper tries to contribute to the general discussion on discourse connectives, concretely to the question whether it is meaningful to distinguish two separate groups of connectives -i.e."classical" connectives limited to few predefined classes like conjunctions or adverbs (e.g.but) vs. alternative lexicalizations of connectives (i.e.unrestricted expressions and phrases like the reason is, he added, the condition was etc.).In this respect, the paper focuses on one group of these broader connectives in Czech -the selected verbs of saying doplnit/doplňovat (to complement), upřesnit/upřesňovat (to specify), dodat/dodávat (to add), pokračovat (to continue) -and analyses their occurrence and function in texts from the Prague Discourse Treebank.The paper demonstrates that these verbs of saying have a special place within the other connectives, as they contain two items -e.g. he added means and he said so the verb to add contains an information about the relation to the previous context (and) plus the verb of saying (to say).This information led us to a more general observation, i.e. discourse connectives in broader sense do not necessarily connect two pieces of a text but some of them carry the second argument right in their semantics, which "classical" connectives can never do.
The paper introduces a possibility of new research offered by a multi-dimensional annotation of the Prague Dependency Treebank. It focuses on exploitation of the annotation of coreference for the annotation of discourse relations expressed by multiword expressions. It tries to find which as-pect interlinks these linguistic areas and how we can use this interplay in automatic searching for Czech expressions like despite this (navzdory tomu), because of this fact (díky této skutečnosti) functioning as multiword discourse markers. 1
We describe and evaluate the semi-automatic addition of a deep syntactic layer to the French Treebank (Abeillé and Barrier [1]), using an existing scheme (Candito et al. [6]). While some rare or highly ambiguous deep phenomena are handled manually, the remainings are derived using a graph-rewriting system (Ribeyre et al. [22]). Although not manually corrected, we think the resulting Deep Representations can pave the way for the emergence of deep syntactic parsers for French.
The aim of this thesis is to clarify a pivot language based dictionary generating method. It contains an analysis of available lexical databases, comparison and then selection of the most suited for the method. It also contains design and implementation of this method as a tool. In the thesis we include previews from Czech-Japanese dictionary generation.
While annotated treebanks are an invaluable tool in linguistic research, the tree-based form in which corpus search tools often present search results is not necessarily well-suited to the user’s requirements. We argue that a concordance style export of search results, built around a user-identified "key node" in the query, represents a useful synoptic view of the data for the user needing to carry out further manual analysis of query results. We present a first implementation of these 'KNIC' concordances for an Old French corpus, using the TigerSearch treebank search engine integrated into the TXM corpus analysis platform.
This paper mainly introduced the research on constructing Mongolian Treebank based on phrase structure grammar. Having Considered related Mongolian Treebank work and Mongolian words characteristics, we developed a Mongolian syntactic tagset. The tagset includes two kinds of tags. One is syntactic constituent tag and the other is grammatical relation tag. On the basis of the tagset, we developed the Mongolian Treebank auxiliary processing system. Finally, we built a Treebank that contains 3645 sentences and did an experiment on this Treebank.
A relatively new theory of motivation posits that purposeful human behavior may be partly explained by multidimensional individual differences "traits of action" (motives). Its 15 motives can be characterized according to their purpose: individual integrity, competitiveness, and cooperativeness. Existing evidence supports the model on which the motives are based and the reliability and validity of strategies to assess them. This experiment tested whether the hypothetical results of consistent, motivated cooperative and competitive behavior could affect ratings of attractiveness. Male and female participants (N = 98; M age = 18.8, SD = 1.4) were shown 24 opposite-sex facial photos ranging in attractiveness. The photos were paired with one of three conditions representing theoretical outcomes that would result from low, control, and high levels of cooperative and competitive motives. As predicted, outcome descriptions representing high motive strength of six motives statistically significantly affected ratings of attractiveness. This result was independent of sex of participant and consistent with the theory.