Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
German genitive attributes are usually tagged as such in treebanks. However, it is well known that this information is not sufficient for determining the type of relation between head nouns and attributes, as genitive attributes can express many different semantic relations. Various linguistic classifications have been worked out, but to my knowledge, nobody has so far proposed to apply this linguistic knowledge to a corpus. The challenge here is to come up with a classification that is both easy to verify and sufficiently fine-grained. Using earlier linguistic approaches as guidelines, I propose in this paper a detailed annotation scheme for German genitive attributes based on readily identifiable noun features. First insights from its application to the Smultron Treebank show that it is easy to distinguish between the proposed classes and that my classification of genitive attributes can be related to a more general semantic annotation level.
Dysfunctional emotional processing affects social functioning in patients with schizophrenia. However, the relationship between emotional perception and response in social interaction has not been elucidated. Twenty-seven patients with schizophrenia and 27 normal controls performed a virtual reality social encounter task in which they introduced themselves to avatars expressing happy, neutral, or angry emotions while verbal response duration and onset time were measured and perception of emotional valence and arousal, and state anxiety were rated afterwards. Self-reported trait-affective scale scores and the Positive and Negative Syndrome Scale (PANSS) ratings were also obtained. Patient group significantly underestimated the valence and arousal of angry emotions expressed by an avatar. While valence and arousal ratings of happy avatars were comparable between groups, patient group reported significantly higher state anxiety in response to happy avatars. State anxiety ratings significantly decreased from encounters with neutral to happy avatars in normal controls while no significant decrease was observed in the patient group. The Social Anhedonia Scale and PANSS negative symptom subscale scores (blunted affect, emotional withdrawal, and passive/ apathetic social withdrawal items) were significantly correlated with state anxiety ratings of the encounters with happy avatars. These results suggest that patients with schizophrenia have interference with the experience of pleasure in social interactions which may be associated with negative symptoms.
Alternative paths to linguistic annotation, such as those utilizing games or exploiting the web users, are becoming popular in recent times owing to their very high benefit-to-cost ratios. In this paper, however, we report a case study on POS annotation for Bangla and Hindi, where we observe that reliable linguistic annotation requires not only expert annotators, but also a great deal of supervision. For our hierarchical POS annotation scheme, we find that close supervision and training is necessary at every level of the hierarchy, or equivalently, complexity of the tagset. Nevertheless, an intelligent annotation tool can significantly accelerate the annotation process and increase the inter-annotator agreement for both expert and non-expert annotators. These findings lead us to believe that reliable annotation requiring deep linguistic knowledge (e.g., POS, chunking, Treebank, semantic role labeling) requires expertise and supervision. The focus, therefore, should be on design and development of appropriate annotation tools equipped with machine learning based predictive modules that can significantly boost the productivity of the annotators.
Most of text mining techniques are based on word and/or phrase analysis of the text. The statistical analysis of a term (word or phrase) frequency captures the importance of the term within a document. However, to achieve a more accurate analysis, the underlying mining technique should indicate terms that capture the semantics of the text from which the importance of a term in a sentence and in the document can be derived. Incorporating semantic features from the WordNet lexical database is one of many approaches that have been tried to improve the accuracy of text clustering techniques. A new semantic-based model that analyzes documents based on their meaning is introduced. The proposed model analyzes terms and their corresponding synonyms and/or hypernyms on the sentence and document levels. In this model, if two documents contain different words and these words are semantically related, the proposed model can measure the semantic-based similarity between the two documents. The similarity between documents relies on a new semantic-based similarity measure which is applied to the matching concepts between documents. Experiments using the proposed semantic-based model in text clustering are conducted. Experimental results demonstrate that the newly developed semantic-based model enhances the clustering quality of sets of documents substantially.
This research concerns linguistic variation and Portuguese teaching at school. It is assumed that linguistic pattern is conceived by teachers as an homogeneous norm, so that it is incompatible with linguistic norms students face with in usual text reading and writing activities. Taking into consideration official evaluation of didactic books, scholar reading activities, and sociolinguistic results that prove school interference at students writing performance, this article proposes that Portuguese classes should present variation as complex continua in which is displayed a plurality of norms.
This paper presents a structural statistical machine translation (SSMT) model to deal with the data sparseness problem that occurs as a result of the necessarily small corpus to translate Chinese into Taiwanese Sign Language (TSL). A parallel bilingual corpus was developed, and linguistic information from the Sinica Treebank is adopted for Chinese sentence analysis. The synchronous context free grammar (SCFG) was adopted to convert a Chinese structure to the corresponding TSL structure and then extract a translation memory which comprises the thematic relations between the grammar rules of both structures. In structural translation, the statistical MT (SMT) approach was used to align the thematic roles in the grammar rules and the translation memory provides the reference templates for TSL structure translation. Finally, the agreement information for TSL verbs was labeled for enriching the expressiveness of the translated TSL sequence. Several experiments were conducted to evaluate the translation performance and the communication effectiveness for the deaf. The evaluation results demonstrate that the proposed approach outperforms a baseline statistical MT system using the same small corpus, especially for the translation of long sentences.
We adapt a semantic role parser to the domain of goal-directed speech by creating an artificial treebank from an existing text tree-bank. We use a three-component model that includes distributional models from both target and source domains. We show that we improve the parser's performance on utterances collected from human-machine dialogues by training on the artificially created data without loss of performance on the text treebank.
Purpose The purpose of this paper is to investigate the characteristics of social network comments to give a broad overview to serve as a baseline for future research. Design/methodology/approach English comments from a representative sample of public MySpace profiles were examined with a collection of exploratory analyses, using automatic data processing, quantitative techniques and content analyses. Findings Comments were normally for general friendship maintenance and were typically short, with 95 per cent having 57 or fewer words. They contained a combination of standard spelling, apparently accidental mistakes, slang, sentence fragments, “typographic slang” and interjections. Several new creative spelling variants derived from previous forms of computer‐mediated communication have become extremely common, including u, ur,:), haha and lol. The vast majority of comments (97 per cent) contained at least one non‐standard language feature, suggesting that members almost universally recognise the informal nature of this kind of messaging. Research limitations/implications The investigation only covered MySpace and only analysed English comments. Practical implications MySpace comments should not be written in, or judged by, standard linguistic norms and may cause special problems for information retrieval. Originality/value This is the first large‐scale study of language in social network comments.
Bond ratings on state‐issued debt provide a signal to credit markets that help them charge an appropriate interest rate, based on the risk of payment default. Though actual default may occur only in extreme circumstances, observed differences in ratings and interest costs across states and time demonstrate that a sound economy, strong financials, and stable policies matter. When data on the factors that presumably affect ratings is public and easily accessible, making sense of differences of opinion between bond rating agencies is difficult. We suggest that such differences—observed as so‐called split bond ratings—are often ephemeral. Utilizing a simulation method to uncover the latent credit risk presented by each state, we show that split ratings on state bonds are often due to the fact that presumed category overlap between rating agencies is absent when evaluated on a common latent scale. Most observed state bond rating splits from 1997 through 2006 can be explained by this category mismatch. Our approach has broad implications for pricing state debt, as well as pricing rated debt in other capital market sectors.
A known problem of WordNet is that it is too ne-grained in its sense denitions. For instance, it does not distinguish between homographs and polysemes. This distinction is crucial in many natural language processing tasks. In this paper we propose to distinguish only between homographs withinWordNet data while merging all polysemous senses. The ultimate goal of this exercise is to compute a more coarsegrained version of linguistic database. In order to achieve this task we propose to merge all polysemous senses according to similarity scores computed by a hybrid algorithm. The key idea of the algorithm is to combine the similarity scores produced by diverse semantic similarity algorithms. We implemented the algorithm and evaluated it on the dataset extracted from the WordNet. The evaluation results are promising in comparison to the other state of the art approaches.
Reflection on testimony provides novel arguments for anti-individualism. What is anti-individualism? Sanford Goldberg's book defends three main claims under this heading: first, facts about the contents of beliefs do not supervene on individualistic facts about the believers (Conclusion 6: 106); second, an individual's epistemic entitlement to accept a piece of testimony depends on facts about her peers (AI-J: 150); third, processes by which some humans acquire knowledge from testimony includes activities performed for them by others (AI-P: 234). Each of these three claims is argued for separately from premises about the ways in which humans, adult and child, succeed in gaining knowledge by testimony. The three arguments provide the structure for Anti-Individualism. Goldberg's argument for the first claim – that the contents of beliefs do not supervene on individualistic facts about the believers – begins from uncontroversial facts about testimony. Gaining knowledge by testimony depends on knowing the meanings of some utterances, and it is possible to know the meaning of a person's utterance while knowing little that would distinguish this person from anyone else (56). The best explanation of how this is possible involves postulating linguistic norms which entail facts about the linguistic meanings of utterances (41). Now in some cases of testimony, a hearer acquires a belief whose content is the linguistic meaning of a speaker's utterance, where the linguistic meaning of this utterance is determined by linguistic norms (Premises 2–4 and Conclusion 3: 105). But linguistic norms might have been different from what they are even while the person's and her audience's non-relational properties remained unaltered (Premises 1: 104–5). And in such a case, the hearer would have acquired a belief with a different content (Premises 5–7: 105–6). Therefore, ‘Psychological facts such as believing that p do not supervene on the individualistic facts regarding the subject’ (106).
Although body image is often conceptualized as a trait, there is growing recognition of situational influences. It was hypothesized that college women whose body image was more reactive to situational influences would be more likely to experience adverse weight-related outcomes, including obesity and binge eating behavior. In a pilot study, vignettes were rated to yield a range of situational body image ratings, and the three vignettes that captured the full range (lowest, median, and highest ratings) were used in the main study. In the main study, 138 female undergraduates (M = 19.5 yr., SD = 1.9; 74.6% "White or Caucasian") completed a survey that included the Questionnaire on Eating and Weight Patterns-Revised. They also provided Body Image State Scale ratings in response to three vignettes drawn from the pilot study. Body image "reactivity" was defined as the range over which body image scores varied for a given participant (standard deviation of the means). Contrary to predictions, overall body dissatisfaction across situations--but not situational reactivity--was related to higher BMI, greater likelihood of binge eating, and higher depression. Results suggest when body image dissatisfaction is relatively impervious to situational influences, it may be a risk factor for disordered eating behavior, whereas body image dissatisfaction that is responsive to situational influences appears to be less pathognomonic.
Abstract. Finding information about companies on multiple sources on the Web has become increasingly important for business analysts. In particular, since the emergence of the Web 2.0, opinions about companies and their services or products need to be found and distilled in order to create an accurate picture of a business entity. Without appropriate text mining tools, company analysts would have to read hundreds of textual reports, newspaper articles, forums’ postings and manually dig out factual as well as subjective information. This paper describes a series of experiments to assess the value of a number of lexical, morpho-syntactic, and sentiment-based features derived from linguistic processing and from an existing lexical database for the classification of evaluative texts. The paper describes experiments carried out with two different web sources: one source contains positive and negative opinions while the other contains fine grain classifications in a 5-point qualitative scale. The results obtain are positive and in line with current research in the area. Our aim is to use the result of classification in a practical application that will combine factual and opinionated information in order to create the reputation of a business entity. 1
In this paper we present a question answering system supported by semantic graphs. Aside from providing answers to natural language questions, the system offers explanations for these answers via a visual representation of documents, their associated list of facts described by subject – verb – object triplets, and their summaries. The triplets, automatically extracted from the Penn Treebank parse tree obtained for each sentence in the document collection, can be searched, and we have implemented a question answering system to serve as a natural language interface to this search. The vocabulary of questions is general because it is not limited to a specific domain, however the questions's grammatical structure is restricted to a predetermined template because our system can understand only a limited number of question types. The answers are retrieved from the set of facts, and they are supported by sentences and their corresponding document. The document overview, comprising the semantic representation of the document generated in the form of a semantic graph, the list of facts it contains and its automatically derived summary, offers an explanation to each answer. The extracted triplets are further refined by assigning the corresponding co referenced named entity, by resolving pronominal anaphors, as well as attaching the associated WordNet synset. The semantic graph belonging to the document is developed based on the enhanced triplets while the document summary is automatically generated from the semantic description of the document and the extracted facts.
This paper presents a comparative study of Judgment and Assessing frames in English and Portuguese. The aim is to verify the possibility of using the FrameNet frames to construct a lexical database for Brazilian Portuguese. The research corpus is composed by 50 legal documents, totalizing 1.055,535 tokens and 39,108 types. Through a contrastive method the Judgment and Assessing frames were selected and translation equivalents for the English lexical units were established. The points considered in this research were the polysemy and the semantic relations of words. The polysemy is the main difficulty in applying FrameNet frames for Portuguese description.
In the article we compare the role of the dictionary and the lexical database, and address the issue of language register and correctness in dictionaries. We then deal with various types of sense distribution in dictionaries, the history of the word, and the principles of selection of dictionary headwords. We cite the corpus as an essential source for the treatment of meaning, collocation and syntagmatics, and investigate ways of interpreting corpus data – corpus profiling of headwords. We conclude with the thought that a dictionary represents the central language standard, whereby all of the expressed linguistic opinions contained in it must be based on corpus evidence.
We present GLARF, a framework for representing three linguistic levels and systems for generating this representation. We focus on a logical level, like LFG's F-structure, but compatible with Penn Treebanks. While less finegrained than typical semantic role labeling approaches, our logical structure has several advantages: (1) it includes all words in all sentences, regardless of part of speech or semantic domain; and (2) it is easier to produce accurately. Our systems achieve 90% for English/Japanese News and 74.5% for Chinese News -these F-scores are nearly the same as those achieved for treebank-based parsing.
Abstract An important assumption underlying most if not all methods of dialectometry is that the automated analysis of the differences in language use between different locations, as they are recorded by dialectologists in large scale surveys, can reveal patterns which directly reflect regional variation. Focusing on lexical variation, this chapter examines the role of concept characteristics in lexical dialectometry in three consecutive logical steps. First, it conducts a regression analysis of data taken from a large lexical database of Limburgish dialects in Belgium and the Netherlands to show that concept characteristics such as concept salience, concept vagueness and negative affect contribute to the lexical heterogeneity in the dialect data. Next, it demonstrates that the relationship between concept characteristics and lexical heterogeneity influences the results of conventional lexical dialectometric measurements. Finally, the chapter proposes a lexical dialectometric method in which concept characteristics form the basis of a weighting schema that determines to which extent concept specific dissimilarities can contribute to the aggregate dissimilarities between locations.
The Arabic language has a very rich/complex morphology. Each Arabic word is composed of zero or more prefixes, one stem and zero or more suffixes. Consequently, the Arabic data is sparse compared to other languages such as English, and it is necessary to conduct word segmentation before any natural language processing task. Therefore, the word-segmentation step is worth a deeper study since it is a preprocessing step which shall have a significant impact on all the steps coming afterward. In this article, we present an Arabic mention detection system that has very competitive results in the recent Automatic Content Extraction (ACE) evaluation campaign. We investigate the impact of different segmentation schemes on Arabic mention detection systems and we show how these systems may benefit from more than one segmentation scheme. We report the performance of several mention detection models using different kinds of possible and known segmentation schemes for Arabic text: punctuation separation, Arabic Treebank, and morphological and character-level segmentations. We show that the combination of competitive segmentation styles leads to a better performance. Results indicate a statistically significant improvement when Arabic Treebank and morphological segmentations are combined.
In this paper, we propose a modular cascaded approach to data driven dependency parsing. Each module or layer leading to the complete parse produces a linguistically valid partial parse. We do this by introducing an artificial root node in the dependency structure of a sentence and by catering to distinct dependency label sets that reflect the function of the set internal labels vis-a¿-vis a distinct and identifiable linguistic unit, at different layers. The linguistic unit in our approach is a clause. Output (partial parse) from each layer can be accessed independently. We applied this approach to Hindi, a morphologically rich free word order language using MST parser. We did all our experiments on a part of Hyderabad Dependency Treebank. The final results show an increase of 1.35% in unlabeled attachment and 1.36% in labeled attachment accuracies over state-of-the-art data driven Hindi parser.
It is unlikely that Standard Afrikaans has been based on one relatively uniform vernacular. Ana Deumert has convincingly argued that what we recognise as Standard Afrikaans today is a construction to be attributed to language entrepreneurs who strove for a unique South African identity towards the end of the nineteenth and early 20th century. This led to deliberately discarding some of the then metropolitan Dutch linguistic norms. The Afrikaans negative and diminutive systems will be shown to be the linguistic outcome of these conceptions of identity and purity.
We study the influence that image features may have on music tension and liveliness perception. 72 music excerpts from different genres and periods were selected, and 72 still shots were taken from different animation features little known to the subjects. 62 subjects rated the isolated images for tension and liveliness, 37 subjects rated the isolated music excerpts for tension and liveliness, and 153 subjects rated the music excerpts combined with the images for music tension and liveliness, and for music-image congruence. There is a significant variation of tension and liveliness of the music as a function of the tension and liveliness of the pairing image, showing a transfer of mood from image to music. The significance of ANOVA tests showed that 40% of music excerpts were image-sensitive for liveliness and 32% for tension. The transfer of mood was dependent on congruence: music excerpts with high congruence with the image had a higher correlation in tension and liveliness rating deviations with the image ratings. For low congruence, the liveliness correlation was not significant and the tension deviation was negatively correlated with the image tension. Feature transfer from image to music depends on the image-music congruence rated by each subject.
The goal of the presented project is to assign a structure of clauses to Czech sentences from the Prague Dependency Treebank (PDT) as a new layer of syntactic annotation, a layer of clause structure. The annotation is based on the concept of segments, linguistically motivated and easily automatically detectable units. The task of the annotators is to identify relations among segments, especially relations of super/subordination, coordination, apposition and parenthesis. Then they identify individual clauses forming complex sentences.
In many languages general syntactic cues are insufficient to disambiguate crucial relations in the task of Parsing. In such cases semantics is necessary. In this paper we show the effect of minimal semantics on parsing. We did experiments on Hindi, a morphologically rich free word order language to show this effect. We conducted experiments with the two data-driven parsers MSTPaser and MaltParser. We did all the experiments on a part of Hyderabad Dependency Treebank. With the introduction of minimal semantics we achieved an increase of 1.65% and 2.01% in labeled attachment score and labeled accuracy respectively over state-of-the-art data driven dependency parser.
In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words.We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an error-driven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus. We describe an efficient framework for training our model based on the Margin Infused Relaxed Algorithm (MIRA), evaluate our approach on the Penn Chinese Treebank, and show that it achieves superior performance compared to the state-of-the-art approaches reported in the literature.
Riflessioni sulle implicazioni didattiche e di ricerca degli strumenti di analisi della linguistica dei corpora con particolare riguardo alla linguistica computazionale (Treebank) per l'insegnamento dell'italiano come L2.
In this paper we describe and evaluate a top-down transfer component of a hybrid example-based machine translation system with an architecture similar to that of transfer MT systems, but with automatically derived transfer-rules and dictionary entries based on a parallel treebank. The tests were applied on the translation pair Dutch to English. Evaluation and error analysis have shown that the top-down transfer process has a number of shortcomings on which we wish to report and which we will try to solve in future work by applying bottom-up transfer.
This paper describes and compares two algorithms that take as input a shared PCFG parse forest and produce shared forests that contain exactly the n most likely trees of the initial forest. Such forests are suitable for subsequent processing, such as (some types of) reranking or LFG f-structure computation, that can be performed ontop of a shared forest, but that may have a high (e.g., exponential) complexity w.r.t. the number of trees contained in the forest. We evaluate the performances of both algorithms on real-scale NLP forests generated with a PCFG extracted from the Penn Treebank.
The present study examined the degree to which acceptance, reappraisal, or suppression based strategies are associated with changes in heart rate, eyeblink startle magnitude, Event-Related Potentials (ERPs), and self-reports of subjective experience in a sample of college undergraduates. Participants were randomly assigned to use one of these strategies during an associative learning task that contained stimuli that signaled either threat or safety from a noxious stimulus as well as during exposure to highly arousing pleasant and unpleasant images. Participants in the reappraisal and suppression groups displayed greater eyeblink startle magnitudes during the emotion induction procedures compared with participants in the acceptance and control groups. No group differences were found with respect to heart rate or ERPs in response to the emotion inductions. Compared with participants assigned to the acceptance and control conditions, participants assigned to the reappraisal and suppression conditions rated unpleasant images as being less unpleasant; however, the groups did not differ in arousal ratings. Participants did not differ in their ratings of discomfort during the associative learning task, nor did they differ in their valence and arousal ratings for pleasant pictures. Findings suggest a possible dissociation of cognition and physiological reactivity for participants using reappraisal and suppression strategies to regulate mood and affect.
CONTEXT: Although drug cues reliably activate the brain's reward system, studies rarely examine how the processing of drug stimuli compares with natural reinforcers or relates to clinical outcomes. OBJECTIVES: To determine hedonic responses to natural and drug reinforcers in long-term heroin users and to examine the utility of these responses in predicting future heroin use. DESIGN: Prospective design examining experiential, expressive, reflex modulation, and cortical/attentional responses to opiate-related and affective stimuli. The opiate-dependent group was reassessed a median of 6 months after testing to determine their level of heroin use during the intervening period. SETTING: Community drug and alcohol services and a clinical research facility. PARTICIPANTS: Thirty-three opiate-dependent individuals (mean age, 31.6 years) with stabilized opiate-substitution pharmacotherapy and 19 sex- and age-matched healthy non-drug users (mean age, 30 years). MAIN OUTCOME MEASURES: Self-ratings, facial electromyography, startle-elicited postauricular reflex, and event-related potentials combined with measures of heroin use at baseline and follow-up. RESULTS: Relative to the control group, the opiate-dependent group rated pleasant pictures as less arousing and showed increased corrugator activity, less postauricular potentiation, and decreased startle-elicited P300 attenuation while viewing pleasant pictures. The opiate-dependent group rated the drug-related pictures as more pleasant and arousing, and demonstrated greater startle-elicited P300 attenuation while viewing them. Although a startle-elicited P300 amplitude response to pleasant (relative to drug-related) pictures significantly predicted regular (at least weekly) heroin use at follow-up, subjective valence ratings of pleasant pictures remained the superior predictor of use after controlling for baseline craving and heroin use. CONCLUSIONS: Heroin users demonstrated reduced responsiveness to natural reinforcers across a range of psychophysiological measures. Subjective rating of pleasant pictures robustly predicted future heroin use. Our findings highlight the importance of targeting anhedonic symptoms within clinical treatment settings.
Cognitive factors such as catastrophic thoughts regarding pain, and conversely, one's acceptance of that pain, may affect emotional functioning among persons with chronic pain conditions. The aims of the present study were to examine the effects of both catastrophizing and acceptance on affective ratings of experimentally induced ischemic pain and also self-reports of depressive symptoms. Sixty-seven individuals with chronic back pain completed self-report measures of catastrophizing, acceptance, and depressive symptoms. In addition, participants underwent an ischemic pain induction procedure and were asked to rate the induced pain. Catastrophizing showed significant effects on sensory and intensity but not affective ratings of the induced pain. Acceptance did not show any significant associations, when catastrophizing was also in the model, with any form of ratings of the induced pain. Catastrophizing, but not acceptance, was also significantly associated with self-reported depressive symptoms when these two variables were both included in a regression model. Overall, results indicate negative thought patterns such as catastrophizing appear to be more closely related to outcomes of perceived pain severity and affect in persons with chronic pain exposed to an experimental laboratory pain stimulus than does more positive patterns as reflected in measures of acceptance.
Semantic processing represents the new challenge for all applications that require text understanding, as for instance Q/A. In this paper we will highlight the need to couple statistical approaches with deep linguistic processing and will focus on ldquoimplicitrdquo or lexically unexpressed linguistic elements that are nonetheless necessary for a complete semantic interpretation of a text. We will address the following types of ldquoimplicitrdquo entities and events: - grammatical ones, as suggested by a linguistic theories like LFG or similar generative theories; - semantic ones suggested in the FrameNet project, i.e. CNI, DNI, INI; - pragmatic ones: here we will present a theory and an implementation for the recovery of implicit entities and events of (non-) standard implicatures. In particular we will show how the use of commonsense knowledge may fruitfully contribute in finding relevant implied meanings. We will also briefly explore the subject of point of view which is computed by semantic informational structure and contributes the intended entity from whose point of view is expressed a given subjective statement. We also present an evaluation based on section 24 of Penn Treebank as encoded by LFG people in the PARC-700 treebank where lexically unexpressed are adequately classified and diversified.
BACKGROUND: There is an interest in investigating the relation between emotional memory impairments in schizophrenia and specific symptom dimensions. We explored potential links between emotional memory and social anhedonia severity in patients with schizophrenia and in healthy individuals. METHODS: Twenty-nine patients with schizophrenia and 27 matched healthy individuals completed the Chapman Revised Social Anhedonia Scale and then performed an emotional face recognition memory task involving happy, sad and neutral face expressions. We calculated emotional memory performance using 2 independent measures: the discrimination accuracy index Pr and the response bias Br. We also measured valence ratings of the face stimuli. We performed correlation analyses using the inter-individual variability in social anhedonia severity and the individual score obtained for each memory performance variable and for each face valence rating condition. RESULTS: Patients with schizophrenia reported higher levels of social anhedonia compared with healthy individuals. They also showed lower recognition accuracy for faces compared with healthy participants. We found no significant correlation between social anhedonia severity and any of the memory performance variables for both patients with schizophrenia and healthy individuals. Regarding potential links between social anhedonia severity and face valence ratings, we found that individuals with elevated social anhedonia had a tendency to rate the face stimuli as more negative. LIMITATIONS: Our negative finding may be partly explained by a lack of statistical power owing to our small patient sample. In addition, our patient sample had unusually high estimated IQ scores, which highlights potential issues regarding the generalization of our findings. Finally, we used a yes-no recognition memory task with a very short retention interval delay. CONCLUSION: Our results suggest that social anhedonia is not directly linked to emotional memory deficits and biases and does not interfere with the modulatory effect of positively valenced emotion on memory.
This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semi-supervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first extension is to combine SS-SCMs with another semi-supervised approach, described in (Koo et al., 2008). The second extension is to apply the approach to second-order parsing models, such as those described in (Carreras, 2007), using a two-stage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Tree-bank for Czech. Our best results on test data in the above datasets achieve 93.79% parent-prediction accuracy for English, and 88.05% for Czech.
Jointly parsing two languages has been shown to improve accuracies on either or both sides. However, its search space is much bigger than the monolingual case, forcing existing approaches to employ complicated modeling and crude approximations. Here we propose a much simpler alternative, bilingually-constrained monolingual parsing, where a source-language parser learns to exploit reorderings as additional observation, but not bothering to build the target-side tree as well. We show specifically how to enhance a shift-reduce dependency parser with alignment features to resolve shift-reduce conflicts. Experiments on the bilingual portion of Chinese Treebank show that, with just 3 bilingual features, we can improve parsing accuracies by 0.6% (absolute) for both English and Chinese over a state-of-the-art baseline, with negligible (~6%) efficiency overhead, thus much faster than biparsing.
This paper presents preliminary investigations on the statistical parsing of French by bringing a complete evaluation on French data of the main probabilistic lexicalized and unlexicalized parsers first designed on the Penn Treebank. We adapted the parsers on the two existing treebanks of French (Abeillé et al., 2003; Schluter and van Genabith, 2007). To our knowledge, mostly all of the results reported here are state-of-the-art for the constituent parsing of French on every available treebank. Regarding the algorithms, the comparisons show that lexicalized parsing models are outperformed by the unlexicalized Berkeley parser. Regarding the treebanks, we observe that, depending on the parsing model, a tag set with specific features has direct influence over evaluation results. We show that the adapted lexicalized parsers do not share the same sensitivity towards the amount of lexical material used for training, thus questioning the relevance of using only one lexicalized model to study the usefulness of lexicalization for the parsing of French.
Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands We present a coding system combined with an annotation tool for the analysis of gestural behavior. The NEUROGES coding system consists of three modules that progress from gesture kinetics to gesture function. Grounded on empirical neuropsychological and psychological studies, the theoretical assumption behind NEUROGES is that its main kinetic and functional movement categories are differentially associated with specific cognitive, emotional, and interactive functions. ELAN is a free, multimodal annotation tool for digital audio and video media. It supports multileveled transcription and complies with such standards as XML and Unicode. ELAN allows gesture categories to be stored with associated vocabularies that are reusable by means of template files. The combination of the NEUROGES coding system and the annotation tool ELAN creates an effective tool for empirical research on gestural behavior.
The present study demonstrates how the emotional content of search terms and their eventual results affects the breadth of a users’ search for information. We observed the quantity of results selected by users. In a random sample of queries from the Microsoft LiveSearch search engine, 7,021 queries were evaluated using a dictionary with valence and arousal ratings. The number of search results selected was regressed on the valence and arousal of the search terms. We additionally observed users’ selection of results based on the position in the search results. Using the same sample, result placement was regressed on the valence and arousal level of the search terms. Results from quantity of search results selected shows that negative search terms result in an overall larger number of selections made than positive search terms. For position-based selections, we found that the selection of the first result is affected by an interaction of valence and arousal. Specifically, users were unaffected by the arousal level of negative search terms, but appeared to be more likely to search deeper on the page when they searched for less arousing positive information. These results suggest that the emotional content associated with a search query may lead users to be more or less discriminating in their acceptance of information and may influence the impact of placement on result selection.
Language can serve as a potent and injurious tool against ambitious women in the workplace. Consider how often both men and women have thought or said 'bitch' or other derogatory terms when describing a woman in a leadership position. Freud once described mental health as the ability to love and to work. For many of these women, their work has also become their love, their passion. Working long hours, often working 'twice as hard as men,' to be viewed as equally competent, perhaps sacrificing spouse and children in deference to career, these women have been inculcated into a patriarchal corporate environment, one in which they must act in a sexual dissonant manner in order to succeed. And as they negotiate the male managerial model, they often come to view their own femininity in an objectified, disparaging way. They may defeminize their language and adopt a 'more adversarial, information-focused style characteristic of all male talk'. Here is where the dilemma begins: If they conform to the masculine linguistic norms of the corporate environment, then their behavior can be perceived as confrontational, harsh, contentious, un-lady-like. A collision of culture ensues on intrapsychic and interpersonal levels between what is expected of a woman in society and what is expected of a person in a high status position where valued leadership traits are male-gendered. Often these women select language and styles that veer away from the feminine side of the gender spectrum. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Pp. xiii, 265, Cambridge, Cambridge University Press, 2007, $90.00. This book is not gracefully written, but it is worth penetrating its stylistic carapace if one values tough argument. When I first read the title, the naughty thought occurred to me, what a pleasure it would be to be relieved of one's individual epistemic responsibilities; of ever again having to assume the burden of finding anything out for oneself regarding matters either of fact or of value. But how could one bring off such a feat? Part I, on semantic anti-individualism, begins with an account of the communication of knowledge, and makes a case for the existence of public linguistic norms from the occurrence of successful communication on the one hand, and the existence of misunderstandings on the other. Then he argues from the reality of public linguistic norms to anti-individualism with regard to the language of thought. Part II is concerned with epistemic anti-individualism, and starts applying it to the epistemic dimension of knowledge communication. Objections are mounted which take into account the phenomena of gullibility and rationality. A final chapter recommends what the author calls ‘an ‘active’ epistemic anti-individualism’; in which the reader is given instruction about ‘nearby possible worlds’ in which someone ‘forms the testimonial belief that there is milk in the fridge, under conditions in which there is no milk in the fridge’ (p. 214). Goldberg has a good deal to say on what he calls ‘the consumption of testimony’ (a phrase I find curious, though he frequently uses it). On the acquisition by children of beliefs based on testimony, we seem to have intuitions of which the implications conflict with one another. It seems perverse to deny, on the grounds of her cognitive immaturity, that three-year-old Sally knows that her mother has just bought some ice-cream for dinner, on the basis of what she has been told by her uncle, when in fact her mother has done this. And yet we are also inclined to say that the cognitive immaturity of children of this age makes it impossible for them to have adequate grounds for believing that such testimony is credible, and therefore for knowing the fact in question. Goldberg displays an impressive mastery of the evidence amassed on these matters by empirical psychologists. One of these argues that, up to a certain age, one can indeed be properly said to know through what one is told by another person, even when one has not acquired the mental capacities necessary adequately to evaluate such information. (It appears to me that it is superstitious to believe that there is a ‘yes’ or ‘no’ answer to the question whether Sally knows about the ice-cream or not; in a sense she does, in a sense she doesn't.) The truth on the central topic under consideration, I believe, may properly be summarized something like this. By attending to the testimony of others, we get the hang of a process which is essentially private to each one of us - using our minds to attend to phenomena of sensation or feeling, to hypothesize more or less intelligently, to judge more or less reasonably what is so, and to make more or less responsible decisions accordingly. Some would say that these activities were not essentially private in that, if we had devices for inspecting the interiors of others' brains, we could observe them directly; but I remain unconvinced. Wittgenstein and his followers have demonstrated, I would concede, that if there were not public criteria for the occurrence of private mental acts, we could not talk of them, or probably even undergo or engage in them. But there are such criteria; we know what it is for people to look and sound as though they had just made an observation, or were trying to puzzle something out, or had just come to a decision after moments or months of hesitation. Having once gained the use of our mental faculties via these criteria, we can use them for ourselves, as even the most insensitive or stupid do to some extent, and persons of genius do to an exceptional degree. Our mental performances are nonetheless essentially private acts, of which we are directly aware, and of which we can enhance our awareness by suitably directed attention. The moral is, that the acquisition and cultivation of our capacity to gain knowledge, whether by testimony or otherwise, is a matter of both ‘public’ social influence and ‘private’ individual practice. We must apply this capacity to some extent for ourselves, as individuals, if we are to live reasonably and responsibly. It will not do to deny individualism so thoroughly as to imply the negation of this enormously important fact. There are times when it is proper to be Athanasius contra mundum. The balance of the public and private, the social and individual, contribution to knowledge, is of the essence. If you tip the balance too far in the direction of the public and social, as I think Goldberg's account might lead you to do, you bid fair to cut off at the root all original creativity in science, morality, or the arts.
Written text is one of the fundamental manifestations of human language, and the study of its universal regularities can give clues about how our brains process information and how we, as a society, organize and share it. Among these regularities, only Zipf's law has been explored in depth. Other basic properties, such as the existence of bursts of rare words in specific documents, have only been studied independently of each other and mainly by descriptive models. As a consequence, there is a lack of understanding of linguistic processes as complex emergent phenomena. Beyond Zipf's law for word frequencies, here we focus on burstiness, Heaps' law describing the sublinear growth of vocabulary size with the length of a document, and the topicality of document collections, which encode correlations within and across documents absent in random null models. We introduce and validate a generative model that explains the simultaneous emergence of all these patterns from simple rules. As a r)
Rapid eye movement (REM) sleep and dreaming may be implicated in cross-night adaptation to emotionally negative events. To evaluate the impact of REM sleep deprivation (REMD) and the presence of dream emotions on a possible emotional adaptation (EA) function, 35 healthy subjects randomly assigned to REMD (n = 17; mean age 26.4 +/- 4.3 years) and control (n = 18; mean age 23.7 +/- 4.4 years) groups underwent a partial REMD and control nights in the laboratory, respectively. In the evening preceding and morning following REMD, subjects rated neutral and negative pictures on scales of valence and arousal and EA scores were calculated. Subjects also rated dream emotions using the same scales and a 10-item emotions list. REMD was relatively successful in decreasing REM% on the experimental night, although a mean split procedure was applied to better differentiate subjects high and low in REM%. High and low groups differed - but in a direction contrary to expectations. Subjects high in REMD% showed greater adaptation to negative pictures on arousal ratings than did those low in REMD% (P < 0.05), even after statistically controlling sleep efficiency and awakening times. Subjects above the median on EA(valence) had less intense overall dream negativity (P < 0.005) and dream sadness (P < 0.004) than subjects below the median. A correlation between the emotional intensities of the morning dream and the morning picture ratings supports a possible emotional carry-over effect. REM sleep may enhance morning reactivity to negative emotional stimuli. Further, REM sleep and dreaming may be implicated in different dimensions of cross-night adaptation to negative emotions.
espanolComo es bien sabido, aunque para los hablantes de una lengua las variedades dialectales resulten mas evidentes en los planos lexico, fonetico o fonologico, ellas se advierten en todos los niveles del lenguaje, orbita de la que, por supuesto, no escapa la sintaxis. Asi, en el caso particular del espanol de Buenos Aires, el uso del Preterito Perfecto Compuesto del Modo Indicativo difiere sensiblemente de la norma castellana, a la vez que la conciencia de los hablantes de la lengua respecto de el es practicamente nula: o lo niegan por completo, alegando que prefieren siempre el Preterito Perfecto Simple, o bien aducen que lo emplean segun la norma de Madrid; lo cual, como se vera a lo largo de nuestro trabajo, no resulta de ese modo en ninguno de los dos casos. Asi pues, intentaremos problematizar las cuestiones de norma y uso, en relacion con la conciencia de los hablantes portenos respecto de su empleo de los tiempos pasados. Para ello, partiremos de un trabajo de campo que hemos realizado y que nos ha permitido esbozar algunos matices caracteristicos del uso del tiempo verbal que nos ocupa, es decir, el Preterito Perfecto Compuesto del Modo Indicativo del dialecto rioplatense. EnglishIt is well known that dialectal language variations appear at every level of language including syntax. However, speakers are usually aware of lexical, phonetics, and phonological variations only. In this particular case, as expected, the use of perfect tenses in Buenos Aires (Argentina) is very different from that of Madrid (Spain). The problem is that most Argentinean speakers know how to use the Present Perfect according to Spanish rules they have learned in school, but their speech do not matches their learning. Most Argentinean speakers would say (and they believe) that they do not use the Present Perfect in everyday life, when they actually do, albeit in a different way. That is why I conducted a survey among speakers of all kind of age, in order to distinguish some specific characteristics of the Present Perfect use in rioplatense dialect. Finally, I intend to discuss the concept of language norm and use related to speakers' awareness in Buenos Aires.
OBJECTIVES: This study is the first in a series designed to develop and norm new theoretically motivated sentence tests for children. The purpose was to examine the independent contributions of word frequency (i.e., how often words occur in language) and lexical density (the number of similar sounding words or "neighbors" to a target word) to the perception of key words in the new sentence set. DESIGN: Twenty-four children with normal hearing aged 5 to 12 yrs served as participants; they were divided into four equal age-matched groups. The stimuli consisted of 100 semantically neutral sentences that were 5 to 7 words in length. Each sentence contained 3 key words that were controlled for word frequency and lexical density. Words with few neighbors come from sparse neighborhoods, whereas words with many neighbors come from dense neighborhoods. The key words within a sentence belonged to one of the four lexical categories: (1) high-frequency sparse, (2) low-frequency dense, (3) high-frequency dense, and (4) low-frequency sparse. Participants were administered the sentence list and the 300 key words in isolation at 65 dB SPL. Each participant group was tested in spectrally matched noise at one of the four signal-to-noise ratios (SNRs -2, 0, 2, and 4 dB). The percent of words correctly identified was calculated as a function of SNR, key word context (sentences vs. words), and key word lexical category. RESULTS: SNR had a significant effect on the recognition of key words in sentences and in isolation; performance improved at higher SNRs. There were significant main effects of word frequency and lexical density as well as a significant interaction between the two lexical factors. In isolation, high-frequency words were recognized more accurately than low-frequency words. In both word and sentence contexts, sparse words yielded greater accuracy than dense words, irrespective of word frequency. There was a modest but significant negative correlation between lexical density and the recognition of words in isolation and in sentences. CONCLUSIONS: Word frequency and lexical density seem to influence word recognition independently in children with normal hearing. This is similar to earlier results in adults with normal hearing. In addition, there seems to be an interaction between the two factors, with lexical density being more heavily weighted than word frequency. These results give us further insight into the way children organize and access words from long-term lexical memory in a relational way. Our results showed that lexical effects were most evident at poorer SNRs. This may have important implications for assessing spoken-word recognition performance in children with sensory aids because they typically receive a degraded auditory signal.