Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Investigating local linguistic norms to discover larger patterns of language behaviour has been standard practice in sociolinguistic study. Looking closely at socially salient variables reveals patterns that problematize accepted trajectories of variation as traditional and newly emerging sociolinguistic identities interact. This paper integrates findings from multiple complementary projects to describe the forces influencing the stopping of interdental fricatives (dis ting for this thing), a highly salient marker of Newfoundland English, in and around St. John’s, the province’s major city. In urbanizing communities multivariate analysis reveals variation patterns typical of dialect erosion: older men maintain traditional norms while younger women move toward the standard, especially in linguistically salient contexts. In the same communities, a timing-based approach finds that young women seem to be agentively inserting stopped forms, suggesting that they have adopted a system with fricatives as the default choice. When we contrast urban and rural communities and affiliations, we find a more complex pattern: style shifting is greatest among urban males and rural females. We posit that these seemingly divergent patterns result from efforts by speakers to position themselves within the local social landscape during a period of rapid social change.
Current automatic wrappers using DOM tree and visual properties of data records to extract the required information from the deep web generally have limitations such as the inability to check the similarity of tree structures accurately. Our study shows that data records located in the deep web do not only share similar visual properties and tree structures, but they are also related semantically in their contents. As such we are able to propose an ontological technique using existing lexical database for English (WordNet) for the extraction of data records from deep web pages. Wrappers designed based on ontological technique are able to reduce the number of potential data regions identified for data extraction, thus improve the data extraction accuracy. In this study, we use visual cue from the underlying browser rendering engine to locate and extract the relevant data region from the deep web by measuring the text and image sizes of data records. Experimental results show that our technique is robust and performs better than the existing state of the art wrappers. Unlike existing ontological based wrappers, our wrapper is domain independent and is able to extract wide range of data records with different structures.
In this paper, we present what we believe to be the first data-driven dependency parser for Urdu. The parser was trained and tuned using MaltParser system, a system for data-driven dependency parsing. The Urdu dependency treebank (UDT) is used for training and testing of the Urdu dependency parser, is also presented first time. The UDT contains corpus of 2853 sentences which are annotated at multiple levels such as part-of-speech (POS) level, chunk (phrase level) and dependency relations level. The UDT also contains information about the token counter, head of current token. The annotation is done manually to build UDT. Urdu Dependency Parsing system is evaluated by conducting a series of experiments. All experiments are performed using Maltparser default algorithm with different feature models. Initial, a base line simple feature model consisting word position, word, head and dependency relation is used for Urdu dependency parsing. Then feature model is enhanced by adding part-of-speech (POS) and chunk (Phrase level) information. The results of all parsing experiments are reported. The overall best labeled accuracy (LA) achieved 74.48 % and 90.14 % of unlabeled attachment score (UAS) is achieved. The error analysis is performed by comparing output data with treebank test data which manual parsed to analyze and classify the different types of errors produced by the parser. This is very useful to identify the future directions for future expansion of the treebank and for improving the parsing accuracy. 1.
In the Chinese teaching and research,it often needs to draw syntax tree for analyzing of relationship among compositions of sentence.Drawn syntax tree manually has many defects,such as having huge workload,needing immense storage capacity and so on.This paper proposes a method to generate syntax tree automatically and display the syntax tree in web page by using VML technology.By comparing the syntax trees generated in this paper with the syntax trees from Treebank of PKU,the result shows that the method of coordinate computing proposed in this paper is more precision,which also demonstrates the efficiency of the visualization method.
We propose to analyse semantic similarity in comparable text by matching syntactic trees and labeling the alignments according to one of five semantic similarity relations. We present a Memory-based Graph Matcher (MBGM) that performs both tasks simultaneously as a combination of exhaustive pairwise classification using a memory-based learner, followed by global optimization of the alignments using a combinatorial optimization algorithm. The method is evaluated on a monolingual treebank consisting of comparable Dutch news texts. Results show that it performs substantially above the baseline and close to the human reference.
Change of first language (L1) status from the most stable language to a less accessible language over the life-span of a bilingual individual is termed ‘language attrition’. Such a shift in ease of L1 access has been reported to affect the lexicon (Pelc, 2001) more than other aspects of language. However, whether L1 attrition is affected by reduced L1-strength or increased second language (L2) interference is unresolved. This study was designed to understand the relative contributions of L1-strength (Ebbinghaus, 1885; Paradis, 2001, 2007) and L2-interference (Loftus & Loftus, 1980; Gürel, 2004) towards L1 attrition in L2-dominant bilingual individuals, and how attrition is affected by language use and proficiency. We used a cross-modal (picture-auditory word) and cross-linguistic (Bengali-English) lexical priming paradigm in order to test the nature of L1-L2 interaction in 27 Bengali-English-speaking individuals. Participants were divided into two groups (L1-dominant and L2-dominant) varying in relative L1-L2-proficiency. Familiarity ratings for English words and their translation equivalents permitted generating four word-pair categories: HighEnglish-HighBengali, LowEnglish-LowBengali, HighEnglish-LowBengali and LowEnglish-HighBengali. Reaction time (RT) and Event related potentials (ERPs) were recorded to a syllable-judgment task for the auditory word. Participants also rated themselves on language and reported language use. We hypothesized that if reduced L1-strength affected L1 attrition, Bengali words from all four word-pair categories would elicit longer RTs and larger negative ERPs than English words. In contrast, if L2-interference affected L1 attrition, all Bengali words except ones from the LowEnglish-HighBengali category would elicit longer RTs and larger negative ERPs compared to their English translations. Results showed participants’ L1-use and L2 self-ratings predicted performance in L1. Behavioral data showed longer RTs for Bengali than English in the HighBengali-HighEnglish category and longest RTs for English in the LowEnglish-HighBengali category. ERP data showed greater negativities to English and Bengali words from the category with low familiarity English ratings regardless of their Bengali ratings. Different results from the two measures of the lexical task suggest that each task reflects a different point in the process of lexical access. Overall, findings suggested that L2-interference into L1 plays a larger role in L1 attrition in L2-dominant individuals.
On October 8‐10, 2009, an interdisciplinary group met in Beverley, Massachusetts, to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.
Problem statement: Text summarization takes care of choosing the most significant portions of text and generates coherent summaries that express the main intent of the given document. This study aims to compare the performances of the three text summarization systems developed by the authors with some of the existing Summarization systems available. These three approaches to text summarization are based on semantic nets, fuzzy logic and evolutionary programming respectively. All the three represent approaches to achieve connectionism. Approach: First approach performs Part of Speech (POS) tagging, semantic and pragmatic analysis and cohesion. The second system under discussion was a new extraction based automated system for text summarization using a decision module that employs fuzzy concepts. Third system under consideration was based on a combination of evolutionary, fuzzy and connectionist techniques. Results: Semantic net approach performs better than the MS Word summarizer as far as the semantics of the original text was concerned. To compare our summaries with those of the well known MS Word, Intellexer and Copernic summarizers, we use DUC's human generated summaries as the bench-mark. The results were very encouraging. The second approach based on fuzzy logic results in an efficient system since fuzzy logic mimics decision making of humans. Third system showed promising results as far as precision and F-measure are concerned than all the other approaches. Conclusion: Our first approach used WordNet, a lexical database for English. Unlike other dictionaries, WordNet does not include information about etymology, pronunciation and the forms of irregular verbs and contains only limited information about usage. To overcome this limitation, we developed a new text summarizer based on fuzzy logic. As Text summarization application requires learning ability based on activation, we utilize ANN attribute through a connectionist model to achieve the best results.
In the field of natural language processing (NLP), there often exist multiple corpora with different annotation standards for the same task. In this paper, we take syntactic parsing as a case study and propose a reranking method which is able to make direct use of disparate treebanks simultaneously without using techniques such as treebank conversion. The method proceeds in three steps: 1) build parsers on individual treebanks; 2) use parsers independently to generate n-best lists for each sentence in test set; 3) rerank individual n-best lists which correspond to the same sentence by using consensus information exchanged among these n-best lists. Experimental results on two open Chinese treebanks show that our method significantly outperforms the baseline system by 0.84% and 0.53% respectively.
OBJECTIVES: It is well recognised that medical training can be extremely stressful and that high stress is a risk factor for a wide range of psychological and health-related consequences. The primary aims of this study were to introduce the Medical Student Stress Profile (MSSP) and to demonstrate its psychometric quality as a specific device for auditing medical student stress. Secondary aims were to establish the reliability, construct and criterion validity of this instrument and to explore the relationships between stress, coping, personality, motivation and emotional intelligence in medical students. METHODS: A battery of self-report measures including the MSSP was administered to a sample of 239 undergraduate and graduate-entry medical students. The battery included indices of stress, coping with and proneness to stress, as well as measures of emotional intelligence, motivation style, personality traits, educational environment perception and self-reported symptomatology. Psychometric evaluation of the MSSP was conducted along with a correlation analysis of stress concomitants. RESULTS: The MSSP revealed good psychometric properties and showed a substantial stress load in the participant sample. The pattern of correlations with concomitant measures conformed generally to expectations. Strong cohort effects were observed, which suggest the importance of future investigation into the role of the group in stress amelioration. Stress adversely affects ratings of the educational environment as measured by the Dundee Ready Education Environment Measure. CONCLUSIONS: The MSSP was specifically developed for the medical training context and may have utility for individual and group stress audits of medical students and as a device to inform remedial programmes in stress management in medical education.
This position paper presents META-NORD project which develops Nordic and Baltic part of the European open language resource infrastructure. META-NORD works on assembling, linking across languages, and making widely available the basic language resources used by developers, professionals and researchers to build specific products and applications. Goals of the project, overall approach and specific focus lines on wordnets, terminology resources and treebanks are described.
The Bases of Derivation of Old English Affixed Nouns: Status and Category The aim of this journal article is to carry out a complete analysis of the category, status and patterns of the bases of derivation of Old English affixal nouns. The results of the analysis are discussed in the light of the evolution from stem-formation to word-formation. The corpus of analysis of this research is based on data retrieved from the lexical database of Old English Nerthus, which contains 30170 predicates. 16694 out of these are nouns, of which 4115 are basic and 12579 qualify as non-basic. Within non-basic nouns there are 3488 affixed nouns (351 by prefixation and 3137 by suffixation) and 9091 compound nouns. The line of argumentation is that, under certain circumstances, the existence of more than one base available for the formation of a derivative does not reinforce the explanation of invariable bases; on the contrary, it goes in the direction of variable bases produced by inflectional processes and made ready for derivation. The following conclusions are reached. In the first place, the importance is underlined of formations on stems in Old English, involving, at least, nouns. Secondly, the analysis evidences that the importance of stem-formation in Old English might be higher than has been acknowledged by previous studies. If Old English made extensive use of words as bases of derivation, a single base should be available; if, on the contrary, Old English is still dependent on stem-formation, more than one base is likely to be found for a single derivative. Such alternative bases of derivation reflect stemformation that may result from inflectional means and be eventually used for derivational purposes.
PURPOSE: To prospectively evaluate the clinical effectiveness of snapshot inversion recovery (SNAPIR), which is a dedicated optimized inversion-recovery-prepared single-shot fast spin-echo T1-weighted sequence, in the delineation of normal fetal brain anatomy compared with that of the currently used T1-weighted gradient-echo protocol, which often yields images of poor quality due to motion artifacts and inadequate contrast. MATERIALS AND METHODS: This study was approved by the hospital research ethics committee, and informed written consent was obtained from all patients. Forty-one fetuses were examined at 19-37 weeks gestation (mean, 29 weeks gestation) by using both the standard T1-weighted protocol and the optimized T1-weighted SNAPIR protocol with a 1.5-T imager. Two independent blinded observers performed qualitative analysis, evaluating overall diagnostic quality, detailed anatomic delineation, and severity of motion artifacts. Quantitative analysis comprised calculation of contrast ratios (CRs) for the cortical gray matter, subplate, white matter, and cerebrospinal fluid. The Wilcoxon signed rank test was used to compare image rating scores, the paired t test was used to compare CRs, and κ statistics were used to test interobserver agreement. RESULTS: Both overall diagnostic quality (P <.001) and detailed anatomic delineation (P <.001) were enhanced with SNAPIR compared with the standard T1-weighted acquisition. Also, motion artifacts were less severe (P =.008) and less extensive (P <.001) with SNAPIR. Corresponding CRs were increased with SNAPIR in seven of eight examined regions. CONCLUSION: SNAPIR is a promising robust alternative to the current T1-weighted acquisitions; its role in the detection of disease requires further study.
This paper describes an attempt to build a lexical database for the Yami language, an Austronesian endangered language. As the Yami language documentation and conservation projects have produced substantial corpora, we are now ready to construct the Yami online knowledge database based on the knowledge we have accumulated in the language. In this paper, we propose a model to build the Word Net-like Yami lexical semantics and database. The model is first described in detail, followed by an illustration of an ontology of fish in the implementation phase.
Abstract The aim of the article is to introduce a new approach to verb valency analysis. This approach – full valency – observes properties of verbs which occur solely in actual language usage. The term “full valency” means that all arguments, without distinguishing complements (obligatory arguments governed by the verb) and adjuncts (optional arguments directly dependent on the predicate verb), are taken into account. Because of an expectation that full valency reflects some mechanism which governs verb behaviour in a language, hypotheses concerning (1) the distribution of full valency frames, (2) the relationship between the number of valency frames and the frequency of the verb, and (3) the relationship between the number of valency frames and verb length were tested empirically. To test the hypotheses, a Czech syntactically annotated corpus – the Prague Dependency Treebank – was used.
Accelerated growth of the Internet has enabled users worldwide to share their feelings and experiences. User-generated content (UGC) websites are the most abundant sources of user reviews. Accurately identifying sentiment phrases is essential to understand the expressed opinions in user reviews. To achieve this, part-of-speech (POS) patterns of phrases are useful. However, previous studies for Chinese opinion extraction only translate English POS patterns directly into Chinese for this task without considering the feasibility. Therefore, this work proposes a Chinese opinion extraction method that exploits the observed Sinica Treebank POS patterns for sentiment phrase identification. Sinica Treebank is a widely representative POS corpus for Chinese. The results of preliminary experiments indicate that the proposed method is highly effective in extracting opinions from Chinese UGC reviews.
This paper investigates the mapping between two semantic formalisms, namely the tectogrammatical layer of the Prague Dependency Treebank 2.0 (PDT) and (Robust) Minimal Recursion Semantics ((R)MRS). It is a first attempt to relate the dependency-based annotation scheme of PDT to a compositional semantics approach like (R)MRS. A mapping algorithm that converts PDT trees to (R)MRS structures is developed, associating (R)MRSs to each node on the dependency tree. Furthermore, composition rules are formulated and the relation between dependency in PDT and semantic heads in (R)MRS is analyzed. It turns out that structure and dependencies, morphological categories and some coreferences can be preserved in the target structures. Moreover, valency and free modifications are distinguished using the valency dictionary of PDT as an additional resource. The validation results show that systematically correct underspecified target representations can be obtained by a rule-based mapping approach, which is an indicator that (R)MRS is indeed robust in relation to the formal representation of Czech data. This finding is novel, as Czech, with its free word order and rich morphology, is typologically different than languages analyzed with (R)MRS to date. 1.
We describe a study that examined the distinctiveness of momentary positive and negative affect (PA and NA) and their relationships with blood pressure and heart rate at work in a sample of 67 full-time employees who provided psychological and physiological measurements multiple times a day, over 10 working days. These employees recorded their subjective ratings on personal digital assistant devices programmed specifically for this study, and assessed their blood pressure and heart rate using automatic recording devices, at each measurement period. Results support the distinctiveness of PA and NA. First, within-individual factor analyses confirmed the existence of two underlying factors in the momentary affect ratings. Second, the within-individual correlations between PA and NA scores (-.14), and among the two latent constructs (-.13) were both low in magnitude. Finally, as predicted by theory, NA was significantly related to blood pressure within individuals, whereas PA and blood pressure were unrelated (both PA and NA were related to heart rate within individuals, as predicted).
In the architecture of a natural language processing system based on linguistic knowledge, two types of component are important: the knowledge databases and the processing modules. One of the knowledge databases is the lexical database, which is responsible for providing the lexical unities and its properties to the processing modules. The systems that process two or more languages require bilingual and/or multilingual lexical databases. These databases can be constructed by aligning distinct monolingual databases. In this paper, we present the interlingua and the strategy of aligning the two monolingual databases in REBECA, which only stores concepts from the “wheeled vehicle” domain.
This study examined whether breathing rate affected self-reported pain and emotion following thermal pain stimuli in women with fibromyalgia syndrome (FM: n=27) or age-matched healthy control women (HC: n=25). FM and HC were exposed to low and moderate thermal pain pulses during paced breathing at their normal rate and one-half their normal rate. Thermal pain pulses were presented in four blocks of four trials. Each block included exposure to both mild and moderate pain trials, and periods of both normal and slow paced breathing. Pain intensity and unpleasantness were recorded immediately following each pain trial, and positive and negative affect were assessed at the end of each block of trials. Compared to normal breathing, slow breathing reduced ratings of pain intensity and unpleasantness, particularly for moderately versus mildly painful thermal stimuli. The effects of slow breathing on pain ratings were less reliable for FM patients than for HCs. Slow versus normal breathing decreased negative affect ratings following thermal pain pulses for both groups, and increased positive affect reports, but only for healthy controls with high trait negative affect. Participants who reported higher levels of trait positive affect prior to the experiment showed greater decreases in negative affect as a result of slow versus normal breathing. These experimental findings provide support for prior reports on the benefits of yogic breathing and mindful Zen meditation for pain and depressed affect. However, chronic pain patients may require more guidance to obtain therapeutic benefit from reduced breathing rates.
The paper introduces three chunk parsing tasks carried in current CIPS parsing evaluation workshop(CIPS-ParsEval-2009),which is organized by Tsinghua University and Northeastern University.They are base chunk parsing,functional chunk parsing and event description clause recognition tasks.The designing motivation and the classification standards of these three chunks are discussed in the paper.Based on the detailed syntactic annotations in Tsinghua Chinese Treebank(TCT),three benchmark chunk banks automatically extracted from TCT are built.The evaluation results of top-5 participating systems are also given.The data analysis from their statistics and the comparison with current chunk schemes show some characteristics of these three chunk parsing tasks.
We present several ways of measuring the inter-annotator agreement in the ongoing annotation of semantic inter-sentential discourse relations in the Prague Dependency Treebank (PDT). Two ways have been employed to overcome limitations of measuring the agreement on the exact location of the start/end points of the relations. Both methods – skipping one tree level in the start/end nodes, and the connective-based measure – are focused on a recognition of the existence and of the type of the relations, rather than on fixing the exact positions of the start/end points of the connecting arrows.
This paper proposes how to convert projective dependency structures into flat phrase structures with language-independent syntactic categories, and use a head finder algorithm to convert these phrase structures back into dependency structures. The head finder algorithm is implemented by a maximum entropy approach with constraint information. The converted phrase structures can be parsed using a hierarchical coarse-to-fine method with latent variables. Experimental results show that the approach finds 98.8% heads of all phrases, and our algorithm achieves state-of-the-art dependency parsing performance in English Treebank.
Lexicon-Grammar tables are a very rich syntactic lexicon for the French language. This linguistic database is nevertheless not directly suitable for use by computer programs, as it is incomplete and lacks consistency. Tables are defined on the basis of features which are not explicitly recorded in the lexicon. These features are only described in literature. Our aim is to define for each tables these essential properties to make them usable in various Natural Language Processing (NLP) applications, such as parsing.
This article details a series of carefully designed experiments aiming at evaluating the influence of automatic pre-annotation on the manual part-of-speech annotation of a corpus, both from the quality and the time points of view, with a specific attention drawn to biases. For this purpose, we manually annotated parts of the Penn Treebank corpus (Marcus et al., 1993) under various experimental setups, either from scratch or using various pre-annotations. These experiments confirm and detail the gain in quality observed before (Marcus et al., 1993; Dandapat et al., 2009; Rehbein et al., 2009), while showing that biases do appear and should be taken into account. They finally demonstrate that even a not so accurate tagger can help improving annotation speed. 1
u-tokyo.ac.jp Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human annotators to beforehand create an extensive training corpus, which is a time-consuming and costly process. On the other hand, unlabeled data is abundant and cheap to collect. In this paper, we propose a novel semi-supervised method for discourse relation classification based on the analysis of cooccurring features in unlabeled data, which is then taken into account for extending the feature vectors given to a classifier. Our experimental results on the RST Discourse Treebank corpus and Penn Discourse Treebank indicate that the proposed method brings a significant improvement in classification accuracy and macro-average F-score when small training datasets are used. For instance, with training sets of c.a. 1000 labeled instances, the proposed method brings improvements in accuracy and macro-average F-score up to 50% compared to a baseline classifier. We believe that the proposed method is a first step towards detecting low-occurrence relations, which is useful for domains with a lack of annotated data. 1
This paper proposes an novel approach to annotate function tags for unparsed text. What distinguishes our work from other attempts in such task is that we assign function tags directly basing on lexical information other than on parsed trees. In order to demonstrate the effectiveness and versatility of our method, we investigate two statistical models for automatic annotation, one is log-linear maximum entropy model and the other is margin maximum based support vector machine model, which achieve the best F-score of 82.8 and 86.4 respectively when tested on the text from Penn Chinese Treebank. We also quantity the effect of POS tagger accuracy on system performance. Our results indicate that the function tag types could be determined via flexible and powerful feature representations from words, POS tags and word position indicators, and that, similarly to syntactic parsing, the main difficulty lies in complex constituents with long-distance dependency.
We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed improvement. Such decoding is enabled by: (1) separating full word features from partial word features so that feature templates can be instantiated incrementally, according to whether the current character is separated or appended; (2) deciding the POS-tag of a potential word when its first character is processed. Early-update is used with perceptron training so that the linear model gives a high score to a correct partial candidate as well as a full output. Effective scoring of partial structures allows the decoder to give high accuracy with a small beam-size of 16. In our 10-fold crossvalidation experiments with the Chinese Treebank, our system performed over 10 times as fast as Zhang and Clark (2008) with little accuracy loss. The accuracy of our system on the standard CTB 5 test was competitive with the best in the literature. 1
Auditory displays have been used in both human-machine and computer interfaces. However, the use of non-speech audio in assistive communication for people with language disabilities, or in other applications that employ visual representations, is still under-investigated. In this paper, we introduce SoundNet, a linguistic database that associates natural environmental sounds with words and concepts. A sound labeling study was carried out to verify SoundNet associations and to investigate how well the sounds evoke concepts. A second study was conducted using the verified SoundNet data to explore the power of environmental sounds to convey concepts in sentence contexts, compared with conventional icons and animations. Our results show that sounds can effectively illustrate (especially concrete) concepts and can be applied to assistive interfaces.
From the perspective of translation, Lestrygonians is a chapter that goes in the face of linguistic norm, subverting expectations of standard language use. The chapter consistently shows meaning to be unstable, self-generating along lines of flight, mushrooming around nodes of (semantic) tension. The cohesion of the text, as shown in the sandwich’ passage, depends on a series of such nodes, of linguistic occasions, associations and interfaces; Bloom’s associations and bricolage (or, to use Jean- Michel Rabaté’s pun, brick-holing)1 of words which direct and channel the text’s movements are called forth by the shape of phrases, homonymy and polysemy. Such instability of meaning and of the directions of the production of meaning certainly musters and breeds translation problems: the translator would have to aim at finding/ creating analogous interfaces in the target language (TL) that could account for, and function as points of departure for Bloom’s strings of associations, for the text’s nodes of meaning and tension.
BACKGROUND: While research indicates cognitive biases in individuals vulnerable to bipolar symptoms, the specificity of these biases to the self and others, and to low or high activation states, is underexplored. METHOD: These biases were investigated using individuals with high (n = 24) and low levels (n = 24) of hypomanic personality (HPS) during word rating and free recall of a list of trait words after a positive versus neutral mood induction. RESULTS: The mood induction was not successful. Also, in contrast to the predicted self-serving bias, there was a self-denigratory bias in self-ratings relative to ratings of another person. In post hoc analyses, the study succeeded in producing a rating task of trait words that differentiated between high and low hypomania-prone individuals, as the high HPS group made higher ratings of high activation trait words (e.g. dynamic) to describe positive and neutral attributes regardless of whether it referred to themselves or another person. The high HPS group also showed a negative recall bias, but it was not specific to the self, questioning assumptions made about negative biases in existing research. Furthermore, a strong relationship emerged between greater use of imagery at encoding and greater recall of self-referent, positive, high activation words, suggesting a role for the intensity of images associated with the amplification of emotions in people with bipolar disorder (Holmes et al., 2008). CONCLUSIONS: It seems important to consider various multiple factors in memory bias research in people vulnerable to bipolar disorder, including self versus other encoding, high versus low activated states and the role of mental imagery. Further research is needed to spell out their interactive contribution.
Proceedings of the Ninth International Workshop \non Treebanks and Linguistic Theories. \nEditors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. \nNEALT Proceedings Series, Vol. 9 (2010), 19-30. \n© 2010 The editors and contributors. \nPublished by \nNorthern European Association for Language \nTechnology (NEALT) \nhttp://omilia.uio.no/nealt. \nElectronically published at \nTartu University Library (Estonia) \nhttp://hdl.handle.net/10062/15891.
The greatest American writer Eugene O'Neill (1888- 1953) achieved a recognizable place among the English and American dramatists through the stylistic features of his dramatic language. This study investigates O'Neill's way of handling dramatic language, in Hairy Ape,( the use of grammar from the settled norms in play writing). It endeavors to show how O'Neill manipulates his character's dialogue and the effects of this manipulation.The problem is related to what extent O'Neill's use of language deviates from the linguistic norm, and the levels of linguistic deviation in his style. The study aims to analyze the types of grammatical deviation in O'Neill's 'Hairy Ape'. Moreover, it attempts to justify the reasons behind these deviations (and to support some critical points of view concerning O'Neill's and style in using grammatical deviations).. Deviation as a linguistic phenomenon has an important effect on the readers (and hearers ), if a part of a poem is deviant it becomes especially, noticeable, or perceptually prominent ( Short, 1969: 11 ). Out of the stylistic analysis,it has been found out that the writer uses a language deviated from the literary convention or everyday speech(norm). He uses different types of linguistic deviations to achieve his purposes.
Review by Shannon Sauro, University of Texas at San Antonio Naomi Baron’s (2008) Always On: Language in an Online and Mobile World, provides an overview of trends in digital communication and the impact these trends and practices have for personal interconnectivity, literacy, attitudes towards privacy and ownership, and the ongoing negotiation of linguistic norms. Formatted as a popular book (i.e., each chapter includes social commentary in the form of comics and cartoons, and in-text citation is replaced with chapter notes at the end of the book), Always On has a target audience that includes the non-specialist interested in the ongoing debate regarding the impact of technology on language and society (see, for example, Crystal, 2008) as well as scholars and students involved in research on computer-mediated communication.
Proceedings of the Ninth International Workshop \non Treebanks and Linguistic Theories. \nEditors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. \nNEALT Proceedings Series, Vol. 9 (2010), 67-78. \n© 2010 The editors and contributors. \nPublished by \nNorthern European Association for Language \nTechnology (NEALT) \nhttp://omilia.uio.no/nealt. \nElectronically published at \nTartu University Library (Estonia) \nhttp://hdl.handle.net/10062/15891.
This article investigates a relatively underdeveloped subject in natural language processing---the generation of punctuation marks. From a theoretical perspective, we study 16 Chinese punctuation marks as defined in the Chinese national standard of punctuation usage, and categorize these punctuation marks into three different types according to their syntactic properties. We implement a three-tier maximum entropy model incorporating linguistically-motivated features for generating the commonly used Chinese punctuation marks in unpunctuated sentences output by a surface realizer. Furthermore, we present a method to automatically extract cue words indicating sentence-final punctuation marks as a specialized feature to construct a more precise model. Evaluating on the Penn Chinese Treebank data, the MaxEnt model achieves an f -score of 79.83% for punctuation insertion and 74.61% for punctuation restoration using gold data input, 79.50% for insertion and 73.32% for restoration using parser-based imperfect input. The experiments show that the MaxEnt model significantly outperforms a baseline 5-gram language model that scores 54.99% for punctuation insertion and 52.01% for restoration. We show that our results are not far from human performance on the same task with human insertion f -scores in the range of 81-87% and human restoration in the range of 71-82%. Finally, a manual error analysis of the generation output shows that close to 40% of the mismatched punctuation marks do in fact result in acceptable choices, a fact obscured in the automatic string-matching based evaluation scores.
Ott, N. & R. Ziai (2010). Evaluating dependency parsing performance on german learner language. In M. Dickinson, K. Müürisep & M. Passarotti (eds.), Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Vol. 9 of NEALT Proceeding Series, 175–186.
A commonly held assumption is that processes underlying explicit and implicit memory are distinct. Recent evidence, however, suggests that they may interact more than previously believed. Using the remember-know procedure the current study examines the relation between recollection, a process thought to be exclusive to explicit memory, and performance on two implicit memory tasks, lexical decision and word stem completion. We found that, for both implicit tasks, words that were recollected were associated with greater priming effects than were words given a subsequent familiarity rating or words that had been studied but were not recognised (misses). Broadly, our results suggest that non-voluntary processes underlying explicit memory also benefit priming, a measure of implicit memory. More specifically, given that this benefit was due to a particular aspect of explicit memory (recollection), these results are consistent with some strength models of memory and with Moscovitch's (2008) proposal that recollection is a two-stage process, one rapid and unconscious and the other more effortful and conscious.
We present a new reference Grammar of French (La Grande Grammaire du français), which is a collective project (gathering around fifty contributors), producing a book (about 2200 pages, to be published en 2011) and associated databases. Like the recent reference grammars of the other Romance Languages, it takes into account the important results of the linguistic research of the past thrity years, while aiming at a non specialist audience and avoiding formalization. We differ from existing French grammar by being focused on contemporary French from a purely descriptive point of view, and by taking spoken data into account. We include a description of all the syntactic phenomena, as well as lexical, semantic, pragmatic and prosodic insights, specially as they interact with syntax. The analysis concerns the data from contemporary written French, but also includes data from spoken corpora and regional or non standard French (when accessible). Throughout the grammar, a simple phrase structure grammar is used, in order to maintain a common representation. The analyses are modular with a strict division of labor between morphology, syntax and semantics. From the syntactic point of view, POS are also distinguished from grammatical relations (or functions). The databases include a terminological glossary, different lexical databases for certain POS, certain valence frames and certain semantic classes, and a bibliographical database. 1. Presentation of the project Sizable comprehensive grammars, written by linguists but aiming at a larger audience, have been published for several European languages: for Italian, the Grande
Medium rank clauses, such as participial and infinitival clauses, have been shown in earlier studies to be more frequent in English than in Swedish. In-stead Swedish prefers complete, finite clauses. This constitutes a problem for English-Swedish machine translation. Here I report a study of such construc-tions using the LinES Parallel Treebank. I also show how the dependency annotation in LinES can be used to define clauses of different ranks. 1
We present a probabilistic model extension to the Tesnière Dependency Structure (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects from both constituency and dependency theory. In addition, it makes use of junction structures to handle coordination constructions. We test our model on parsing the English Penn WSJ treebank using a re-ranking framework. This technique allows us to efficiently test our model without needing a specialized parser, and to use the standard evaluation metric on the original Phrase Structure version of the treebank. We obtain encouraging results: we achieve a small improvement over state-of-the-art results when re-ranking a small number of candidate structures, on all the evaluation metrics except for chunking.
Lexicon-Grammar tables are a very rich syntactic lexicon for the French language. This linguistic database is nevertheless not directly suitable for use by computer programs, as it is incomplete and lacks consistency. Tables are defined on the basis of features which are not explicitly recorded in the lexicon. These features are only described in literature. Our aim is to define for each tables these essential properties to make them usable in various Natural Language Processing (NLP) applications, such as parsing.
El marco teórico de Parsing Predictivo Interactivo (IPP) permite construir sistemas de anotación sintáctica interactivos. Los anotadores humanos pueden utilizar estos sistemas de ayuda para crear árboles sintácticos con muy poco esfuerzo (en comparación con el trabajo requerido para corregir manualmente árboles obtenidos a partir de un analizador sintáctico completamente automático). En este artículo se presenta la adaptación a la lengua castellana del marco IPP y su herramienta de anotación IPP-Ann, usando modelos obtenidos a partir del UAM Spanish Treebank. Hemos llevado a cabo experimentación simulando al usuario para obtener métricas de evaluación objetivas para nuestro sistema. Estos resultados muestran que el marco IPP aplicado al UAM Spanish Treebank se traduce en una importante cantidad de esfuerzo ahorrado, comparable con el obtenido al aplicar el marco IPP para analizar la lengua inglesa mediante el Penn Treebank.
Co-constructing communicative effectiveness is often challenging in English as a lingua franca (ELF): speakers have considerably less to go on in terms of shared expectations of cultural knowledge and linguistic norms. A university environment provides a convenient backdrop for sharing at least academic conventions – although these vary more than might be surmised from the uniform labelling of such event types. This paper looks into some discourse and lexicogrammatical features in academic ELF, using ELFA as the database. The data consists of spoken language, which provides direct access to the ways in which meanings are negotiated in ongoing discourse, and the speech events are typically polylogic. ELF discourse requires close cooperation from the participants, which is reflected in its enhanced explicitness among other things. The explicitation strategies speakers display facilitate mutual comprehensibility and contribute to social cohesion within the multi-participant groups. Such strategies also help overcome the potential problems participants might have in dealing with a variety of formal deviations from ordinary English as a native language (ENL). Most of the time ELF bears a very close resemblance to Standard English, but signs of incipient ELF-specific developments are also in evidence.
WordNet is a large lexical database for English. With its broad coverage and a design that is useful for a range of natural-language processing applications, this resource has found wide general acceptance. We offer only a brief description here and refer the reader to Miller, 1990 and Fellbaum, 1998 for further details. WordNet's creation in the mid-1980s was motivated by current theories of human semantic organization (Collins and Quillian, 1969). People have knowledge about tens of thousands of concepts, and the words expressing these concepts must be stored and retrieved in an efficient and economic fashion. A semantic network such as WordNet is an attempt to model one way in which concepts and words could be organized.