Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
We describe how simple, commonly understood statistical models, such as statistical dependency parsers, probabilistic context-free grammars, and word-to-word translation models, can be effectively combined into a unified bilingual parser that jointly searches for the best English parse, Korean parse, and word alignment, where these hidden structures all constrain each other. The model used for parsing is completely factored into the two parsers and the TM, allowing separate parameter estimation. We evaluate our bilingual parser on the Penn Korean Treebank and against several baseline systems and show improvements parsing Korean with very limited labeled data. 1
The goal of the MULI (MUltiLingual Information structure) project is to empirically analyse information structure in German and English newspaper texts. In contrast to other projects in which information structure is annotated and investigated (e.g. in the Prague Dependency Treebank, which mirrors the basic information about the topic-focus articulation of the sentence), we do not annotate theory-biased categories like topic-focus or theme-rheme. Trying to be as theory-independent as possible, we annotate those features which are relevant to information structure and on the basis of which typical patterns, co-occurrences or correlations can be determined. We distinguish between three annotation levels: syntax, discourse and prosody. The data is based on the TIGER Corpus for German and the Penn Treebank for English, since the existing information on part-of-speech and syntactic structure can be re-used for our purposes. The actual annotation of an English example sequence illustrates our choice of categories on each level. Their combination offers the possibility to investigate how information structure is realised and can be interpreted. 1.
Existing treebanks of written language, as e.g., TIGER [2], TuBa-D/Z [11], Penn Treebank [1] etc., usually consist of sentences that can be considered as grammatically well-formed. The SINBAD treebank we present here covers a completely new domain, namely suboptimal syntactic structures, i.e., sentences which are neither fully grammatical nor completely ungrammatical, but merely suboptimal.1 The treebank consists of a collection of German sentences that are rated suboptimal or ungrammatical in the literature, as well as of sentences drawn from our own experimental work on graded grammaticality judgments. In the literature, these structures are usually compared with grammatical structures which express the same meaning, and for ease of comparison these were sometimes included in the treebank as well. With this data collection we provide access to negative evidence which does not occur in ordinary corpora of written or spoken language. It is characteristic for suboptimal structures that these data are judged incoherently varying between different speakers and in different contexts. It is therefore important to provide a systematic collection of these judgments in order to allow researchers better access to past judgements on the phenomena they are interested in and thus contribute towards greater consistency, even in tricky cases. Since most work in syntactic theory is based on suboptimal or ungrammatical structures, the treebank aims at providing linguists with a data basis for their research. This requires a rich syntactic annotation with linguistically relevant concepts. The linguistic framework of the annotation is that of generative grammar in the sense that the trees are strictly binary branching and contain traces and empty categories. The
In information retrieval and text mining, information on word senses is usually taken from dictionaries or lexical databases that have been prepared by lexicographers. We propose an automatic method for word sense induction, i.e. for the discovery of a set of sense descriptors to a given ambiguous word. The approach is based on the statistics of word co-occurrence as derived from Web pages. The underlying assumption is that the senses of an ambiguous word are best described by terms that, although bearing a strong association to this word, are mutually exclusive, i.e. whose association strength within the retrieved Web pages is as weak as possible. Measuring association strength is based upon a novel confidence gain approach that relates the observed co-occurrence frequency for two sense descriptor candidates to an average co-occurrence frequency for pairs of arbitrary words. The proposed approach is fully unsupervised and takes into account the contemporary meanings of words, as reflected in texts from the Internet. Our results are evaluated using a list of ambiguous words commonly referred to in the literature.
The purpose of this paper is to describe the TuBa-D/Z treebank of written German and to compare it to the independently developed TIGER treebank (Brants et al., 2002). Both treebanks, TIGER and TuBa-D/Z, use an annotation framework that is based on phrase structure grammar and that is enhanced by a level of predicate-argument structure. The comparison between the annotation schemes of the two treebanks focuses on the different treatments of free word order and discontinuous constituents in German as well as on differences in phrase-internal annotation.
Rating agencies' track record is good in developed countries but poor in emerging economies. Why? Given the almost-monopolistic structure of the industry, we conjecture that agencies might underinvest in information gathering. We propose an indicator quantifying the agencies' effort to gather information and assess whether greater effort affects rating levels. We detect: (i) absolute underinvestment for non-OECD sovereigns (less effort in spite of greater opaqueness); (ii) relative underinvestment for non-OECD firms compared with OECD ones (though the former receive a larger effort, more intense effort boosts firm ratings in non-OECD countries while depressing them in OECD countries).
The sense of smell has been traditionally assumed to be different from other sensory modalities in that odors are encoded perceptually, without a semantic component. Recent findings of improved odor memory upon encoding with verbal cues question this view. Furthermore, familiar odors are easier to remember and discriminate than are unfamiliar ones, and odor familiarity is reported to predict odor naming. To investigate whether familiar odors are processed by different cerebral structures than those that process unfamiliar odors, (15)O H(2)O-positron emission tomography (PET) measurements of cerebral blood flow were carried out in 14 healthy men. The task was passive, birhinal, smelling of familiar odors (FAM), unfamiliar odors (uFAM), and odorless air (AIR). Significant activations (P < 0.05) were calculated using the contrasts FAM-AIR, uFAM-AIR, and FAM-uFAM, and deactivations running these contrasts in the opposite direction. In relation to AIR, both FAM and uFAM activated amygdala, piriform cortex, and parts of anterior cingulate cortex. FAM activated, in addition, left frontal cortex (Brodmann's areas 44,45,47), left parietal cortex incorporating precuneus, and right parahippocampus. Clusters covering parahippocampus and precuneus were observed also in FAM-uFAM. The activation of left frontal cortex and right parahippocampus was positively correlated with familiarity ratings. Smelling of familiar but not unfamiliar odorants seems to engage cerebral circuits mediating memory and language functions, in addition to the engagement of olfactory cortex. Already the most elemental form of odor processing, passive perception thus seems to engage semantic circuits. This is achieved by the ability of odorants to immediately elicit associations and judgments of odor characteristics.
OBJECTIVE: To validate a culturally relevant body image instrument among urban African Americans through three distinct studies. RESEARCH METHODS AND PROCEDURES: In Study 1, 38 medical practitioners performed content validity tests on the instrument. In Study 2, three research staff rated the body image of 283 African-American public housing residents (75% women, mean age = 44 years), with the residents completing body image, BMI, and percentage body fat measures. In Study 3, 35 African Americans (57% men, mean age = 42) completed body image measures and evaluated their cultural relevance. RESULTS: In Study 1, 97% to 100% of practitioners sorted the jumbled figures into the correct ascending order. The correlation between the body image figures and the practitioners' weight classifications of the figures was high (r = 0.91). In Study 2, observers arrived at similar ratings of body size with excellent consistency (alpha = 0.95). Ratings of body image were strongly correlated with participant BMI (r = 0.89 to 0.93 across observers and 0.81 for all participants) and percentage of body fat (r = 0.77 to 0.89 across observers and 0.76 for all participants). In Study 3, body image ratings with the new scale were positively correlated with other validated figural scales. The majority of participants reported that figures in the new body image scale looked most like themselves and other African Americans and were easiest to identify themselves with. DISCUSSION: The instrument displayed strong psychometric performance and cultural relevance, suggesting that the scale is a promising tool for examining body image and obesity among African Americans.
Recent performance appraisal research has highlighted the important role played by contextual and individual factors in shaping rating behavior. This article reviews cumulated empirical data supporting the proposition that in factors and constraints present in the organization, contexts in which appraisal systems reside and rater attributes, such as personality factors or beliefs, systematically affect rating behavior. The effects of these context and rater factors are reflected in ratings accuracy, ratings discrimination among raters/dimensions, and rating elevation.
Recent evaluation techniques applied to corpus-based systems have been introduced that can predict quantitatively how well surface realizers will generate unseen sentences in isolation. We introduce a similar method for determining the coverage on the Fuf/Surge symbolic surface realizer, report that its coverage and accuracy on the Penn TreeBank is higher than that of a similar statistics-based generator, describe several benefits that can be used in other areas of computational linguistics, and present an updated version of Surge for use in the NLG community.
The epidemic of mesothelioma in Cappadocia, Turkey, is unprecedented in medical history. In three Cappadocian villages, Karain, Tuzkoy and "old" Sarihidir, about 50% of all deaths (including neonatal deaths and traffic fatalities) have been caused by mesothelioma. No other epidemic in medical history has caused such a high incidence of death. This is even more unusual when considering that (I) epidemics are caused by infectious agents, not cancer, and (II) mesothelioma is a rare cancer. World-wide mesothelioma incidence varies between 1/10<sup>6</sup> in areas with no asbestos industry to about 10-30/10<sup>6</sup> in areas with asbestos industry. This article reviews how the mesothelioma epidemic was discovered in Cappadocia by Dr. Baris (my mentor), how we initially linked the epidemic to erionite exposure, and later (with Dr. Carbone) to the interaction between genetic predisposition and environmental exposure. Our team's work had an important positive impact on the lives of those living in Cappadocia and also in many genetically predisposed families living around the world. I will discuss how the work that started in three remote Cappadocian villages led to the award of a NCI P01 grant to support our studies. Our studies proved that genetics modulates mineral fiber carcinogenesis and led to the discovery that carriers of germline <i>BAP1</i> mutations have a very high risk of developing mesothelioma and other malignancies. A new, very active field of research developed following our discoveries to elucidate the mechanism by which <i>BAP1</i> modulates mineral fiber carcinogenesis as well as to identify additional genes that when mutated increase the risk of mesothelioma and other environmentally related cancers. I am the only surviving member of this research team who saw all the phases of this research and I believe it is important to provide an accurate report, which hopefully will inspire others.
This paper introduces the Prague Czech-English Dependency Treebank (PCEDT), a new Czech-English parallel resource suitable for experiments in structural machine translation. We describe the process of building the core parts of the resources – a bilingual syntactically annotated corpus and translation dictionaries. A part of the Penn Treebank has been translated into Czech, the dependency annotation of the Czech translation has been done automatically from plain text. The annotation of Penn Treebank has been tranformed into dependency annotation scheme. A subset of corresponding Czech and English sentences has been annotated by humans. First experiments in Czech-English machine translation using these data have already been carried out. The resources being created at Charles University in Prague are scheduled for release as Linguistic Data Consortium data collection in 2004. 1.
Using a voice key is a popular method for recording vocal response times in a variety of language production tasks. This article describes a class module called VoiceRelay that can be easily utilized in Visual Basic programs for voice key operation. This software-based voice key offers the precision of traditional voice keys (although accuracy is system dependent), as well as the flexibility of volume and sensitivity control. However, VoiceRelay is a considerably less expensive alternative for recording vocal response times because it operates with existing PC hardware and does not require the purchase of external response boxes or additional experiment-generation software. A sample project demonstrating implementation of the VoiceRelay class module may be downloaded from the Psychonomic Society Web archive,www.psychonomic.org/archive.
We introduce a simple method to build Lexicalized Hidden Markov Models (L-HMMs) for improving the precision of part-of-speech tagging. This technique enriches the contextual Language Model taking into account a set of selected words empirically obtained. The evaluation was conducted with different lexicalization criteria on the Penn Treebank corpus using the TnT tagger. This lexicalization obtained about a 6% reduction of the tagging error, on an unseen data test, without reducing the efficiency of the system. We have also studied how the use of linguistic resources, such as dictionaries and morphological analyzers, improves the tagging performance. Furthermore, we have conducted an exhaustive experimental comparison that shows that Lexicalized HMMs yield results which are better than or similar to other state-of-the-art part-of-speech tagging approaches. Finally, we have applied Lexicalized HMMs to the Spanish corpus LexEsp.
In this paper we introduce a naive algorithm for nondeterminisctic LTAG derivation tree extrac-tion from the Penn Treebank and the Proposi-tion Bank. This algorithm is used in the EM models of LTAG Treebank Induction reported in (Shen and Joshi, 2004). Given the trees in the Penn Treebank with PropBank tags, this algorithm generates shared structures that al-low efficient dynamic programming in the EM models. 1
Despite much research, the distinctive personality characteristics of entrepreneurs are yet to be established and the influence of personality on entrepreneurial behaviour remains unclear. This is particularly evident in our understanding of the personal response of entrepreneurs to business failure. In this thesis the Life Story Model of Identity proposed by McAdams' (1993; McAdams & Pals, 2006) narrative theory of personality formed the main theoretical approach to investigating these two related aspects in the psychological understanding of entrepreneurs. This model overcomes some of the limitations of previous personality research by permitting investigation of personality within the entrepreneurial environment and provides a wholistic and complex view of personality as expressed in the entrepreneurs' own words. The model's qualitative methodology and theoretical emphasis on personal meaning making also rendered it most suitable for exploring entrepreneurs' personal response to business failure. McAdams' (1993; McAdams & Pals, 2006) Life Story Interview was employed to explore the self-narrative identities of 40 highly successful entrepreneurs (39 males, one female). Participants were managing directors of businesses sourced from two lists of the fastest growing small to medium companies in Australia, as compiled by the Australian business magazine, the 'Business Review Weekly'. Participants were the founders of their businesses, and had been pursuing entrepreneurship for at least five years. A broad range of business sectors were represented, including computer services, manufacturing, engineering and communications. Prior to interviews, participants completed the Life Story Interview Questionnaire (LSIQ), which was an adapted form of the Life Story Interview that requested written responses to open-ended questions about the content of participants' life stories. A section requesting affective ratings for key events, derived from Herman’s (Hermans & Hermans-Jansen, 1995) Extended List of Affect Terms, was included to further the exploration of life story themes. A second questionnaire, comprised of measures of personality and a measure of psychological symptoms was also completed. During interviews, participants' responses to the LSIQ were discussed, concentrating on further investigation of the key events that defined their life stories. Findings revealed a prototypical life story of the entrepreneur, highlighting distinctive, commonly shared personality characteristics, with much of their selfnarrative identity grounded in experiences within the entrepreneurial environment. The prototypical life story contained a core theme with an agentic-type emphasis on strengthening the self, and a lesser theme with a communion-type emphasis on valuing relationships. Each of these themes comprised two further themes. The selfstrengthening theme included a redemptive theme of overcoming difficulties in a way that left the protagonist feeling stronger and more able to influence their environment, and a positively toned theme of drawing strength and confidence in one's abilities from achievements and successes. The relational theme included a redemptive theme of responding to private relationship difficulties and losses in one area by strengthening other private relationships, and a negatively toned, sometimes contaminated theme, of experiencing either private or professional relational difficulties and losses as irresolvable. The resulting prototypical life story of the entrepreneur was a story centred upon overcoming adversity and celebrating personal achievement, of confirming and boosting confidence in one’s abilities and a sense of personal power to influence their environment. Running parallel to this main storyline was a less prominent plot involving the importance of relationships, with difficulties and losses sometimes redeemed and sometimes left unresolved. To investigate the impact of business failure, participants were asked to describe their experience of business failure as a key life story event during the Life Story Interview. Additional open-ended questions explored important elements of their critical and retrospective responses. A commonly shared personal response was evidenced. Despite being strongly identified with their business at the time, the failure was evaluated in business rather than personal terms. The causes were most often attributed to a combination of internal and external factors, but business recovery was attributed exclusively to their own actions. The self-narrative meanings given to this event centred upon overcoming the business failure in a self-strengthening way as either: mastering business conflict situations; learning entrepreneurial skills; or affirming entrepreneurial self-confidence. In the midst of the failure, most entrepreneurs remained highly optimistic about their chances of success in the future, based largely upon confidence in their ability to bring about business recovery. Practised coping skills were used to manage negative feelings arising from the failure, and an active problem-solving approach was adopted. When reflecting upon their experience, there was an absence of rumination and regret about the business failure. Instead, it was regarded as an inevitable and even welcome event that provided valuable entrepreneurial learning. In making sense of the failure in relation to the rest of their self-narrative identity, most entrepreneurs were able to integrate its meaning within their larger self-story. This was done by relating the business’ recovery to a story of overcoming obstacles, or by containing the business’ failure within a story of either repeated success or sustained self-confidence in one's ability. It was concluded that these entrepreneurs shared a particular type of selfnarrative identity that was conducive to the pursuit of entrepreneurship; positively influencing their behaviour within the entrepreneurial environment and having particular relevance to how they personally responded to business failure. These findings advance understanding of the personality of entrepreneurs, and begin to inform what constitutes a constructive personal response to the event of business failure.
The aim of this study is to highlight what kind of information distinguishes abstract and concrete conceptual knowledge in different aged children. A familiarity-rating task has shown that 8-year-olds judged concrete concepts as very familiar while abstract concepts were judged as much less familiar with ratings increasing substantially from age 10 to age 12, according to literature showing that abstract terms are not mastered until adolescence (Schwanenflugel, 1991). The types of relation elicited by abstract and concrete concepts during development were investigated in an association production task. At all considered age levels, concrete concepts mainly activated attributive and thematic relations as well as, to a much lesser extent, taxonomic relations and stereotypes. Abstract concepts, instead, elicited mainly thematic relations and, to a much lesser extent, examples and taxonomic relations. The patterns of relations elicited were already differentiated by age 8, becoming more specific in abstract concepts with age.
An algorithm is described to efficiently compute the cumulative distribution and probability density functions of the diffusion process (Ratcliff, 1978) with trial-to-trial variability in mean drift rate, starting point, and residual reaction time. Some, but not all, of the integrals appearing in the model’s equations have closed-form solutions, and thus we can avoid computationally expensive numerical approximations. Depending on the number of quadrature nodes used for the remaining numerical integrations, the final algorithm is at least 10 times faster than a classical algorithm using only numerical integration, and the accuracy is slightly higher. Next, we discuss some special cases with an alternative distribution for the residual reaction time or with fewer than three parameters exhibiting trialto-trial variability.
The motivation of the Papillon project is to encourage the development of freely accessible Multilingual Lexical Resources by way of online collaborative work on the Internet. For this, we developed a generic community website originally dedicated to the diffusion and the development of a particular acception based multilingual lexical database.
We present a novel methodology to enhance Chinese text chunking with the aid of transductive Hidden Markov Models (transductive HMMs, henceforth). We consider chunking as a special tagging problem and attempt to utilize, via a number of transformation functions, as much relevant contextual information as possible for model training. These functions enable the models to make use of contextual information to a greater extent and keep us away from costly changes of the original training and tagging process. Each of them results in an individual model with certain pros and cons. Through a number of experiments, we succeed in integrating the best two models into a significantly better one. We carry out the chunking experiments on the HIT Chinese Treebank corpus. Experimental results show that it is an effective approach, achieving an F score of 82.38%.
We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation.1 QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three “shifted” distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibull distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.
This paper describes a new, large scale discourse-level annotation project -- the Penn Discourse TreeBank (PDTB). We present an approach to annotating a level of discourse structure that is based on identifying discourse connectives and their arguments. The PDTB is being built directly on top of the Penn TreeBank and Propbank, thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms.
Positive and negative affects may bias behavior toward approach to rewards and withdrawal from threat, particularly when the contingencies are ambiguous. The hypothesis was that positive and negative affects would associate predictably with identification of happy, disgusted, or angry expressions that may signal potentially rewarding or aversive social interactions. Healthy volunteers (n=86) completed affect ratings and a facial emotion task that employed morphed continua in which emotional expressions gradually decreased in ambiguity. Relations between mood and intensity thresholds for emotion identification were computed. Anhedonia (low positive affect) predicted thresholds for happy expressions (r=0.24; P=.026) whereas negative affect predicted thresholds for disgust (r=-0.25; P=.022). Even within a normal range of mood, mood predicted emotion identification, supporting constructs of positive and negative affect derived originally from self-report measures.
El proyecto que nos ocupa se enmarca dentro de un proyecto global de desarrollo de herramientas de gestion de bases de datos de conocimiento linguistico para su uso academico. Hasta el momento, se habian desarrollado varios proyectos dentro de este ambito, cuyos resultados fueron la modelizacion de las bases de datos necesarias para un diccionario bilingue de terminos, para un diccionario multilingue y para un sistema ontologico. Ademas, se desarrollaron las herramientas de administracion y consulta de un diccionario bilingue de terminos y una herramienta de migracion de este diccionario a un sistema ontologico. Tambien se desarrollaron en anteriores proyectos herramientas de consulta y administracion de un diccionario multilingue asi como la internacionalizacion todas las herramientas que permite ejecutar una herramienta en diferentes PCs, utilizando el idioma de interfaz que tenga definido el usuario en la configuracion regional, potenciandose de esta forma la utilidad y versatilidad de cualquier herramienta, pero muchos cuando estamos tratando como es nuestro caso, de herramientas de gestion del conocimiento linguistico. [ABSTRACT] The project we are working at is part of a global linguistic database modelled management tools research need for a bilingual dictionary of terms, a multilingual dictionary and an ontological system. In addition, administration and consulting tools were developed for a bilingual of terms and a migration tool from this dictionary to an ontological system. Also, in previous projects were developed consulting and administration tools for a multilingual dictionary and the internalisation of all of the tools that allows run a tool into different computers using the default language interface defined in the regional configuration, multiplying the versatility and utility of any tool.
Information exchange is increasing rapidly with the advent of globalization. As the language spoken by the most people in today's world, Chinese will play an important role in information exchange in the future. Therefore, we need an efficient and practical means to access the increasingly large volume of Chinese data. This thesis describes a target-dominant Chinese-English machine translation system, which can translate a given Chinese news sentence into English. We conjecture that we can improve the state of the art of MT using a TDMT approach. This system has participated in the NIST (National Institute of Standards and Technology) 2004 machine translation competition. Experimental results on Penn Chinese Treebank corpus show that a machine translation system adopting a target-dominant approach is promising.
A program called the Generalized Electronic Interviewing System (GEIS) was developed for conducting interviews, using computer-assisted telephone interview (CATI) and interactive voice response (IVR) modes without the need for a programmed interface. GEIS questionnaires were prepared using a common script syntax in all supported modes. Scripted development allowed for rapid interview development without the need for programming. A GEIS script specified the following: question texts, including variable texts; answer option texts; numeric codes for answers; range check information; logical question-branching information; interview status information; do-loop information; and IVR information, such as key codes and voice messages. GEIS thoroughly checked scripts for logical or syntactical errors. GEIS required SAS Version 8.0, and survey data were accumulated within SAS data sets. An application of GEIS to conduct a survey involving CATI, IVR, and a combined hybrid method is described. The CATI results deviated in the direction expected for sensitive questions, whereas IVR obtained a small sample size, rendering the results unreliable. However, the hybrid method was found to provide more accurate telephone survey data on alcohol consumption than did CATI alone. The program may be downloaded from the Psychonomic Society Web archive atwww.psychonomic.org/archive/.
The paper presents an unlexicalized probabilistic parsing model for German trained on the Negra treebank. Evaluation is performed with respect to constituency and dependency measures. It is observed that existing models based on Parent Encoding and Markovization optimize for constituency measures at the expense of dependency performance (at least in German). Several linguistically inspired transformation and annotation schemes are proposed which do help with dependency measures. Finally, it is shown that performance compares well with published results for German.
We present a new methodology for the semiautomated maintenance of a treebank built from analyses of a computational grammar and gauge the effort required for each update cycle. Based on a decade of large-scale grammar engineering experience, we propose a tight integration of treebank maintenance with the continuous evolution of a ‘deep’ computational grammar.
This paper takes a look at how information competency, information processing, and receiver apprehension, affect rating behavior when rating speeches. The paper is broken down into two studies. The first study looks at information competency, which is measured by the Information Competency Assessment Instrument, and how it affects rating behavior. The second study looks at information processing, which is broken down into three scales based on the Inventory of Learning Processes then each is analyzed based on rating behavior, and receiver apprehension which is measured using the Receiver Apprehension Test and how these two concepts affect rating behavior. Students were given surveys to measure information competency, information processing, and receiver apprehension and also asked to evaluate speeches. A negative correlation between receiver apprehension and information processing was found along with support for the idea that information competency and information processing have an affect on rating behavior.
Mapping between syntax and semantics is one of the most promising research topics in corpus annotation. This paper deals with the implementation of an semi-automatic transformation from a syntactically-tagged corpus into a semantic-tagged one. The method has been experimentally applied to a 1600-sentence treebank (the UAM Spanish Treebank). Results of evaluation are provided as well as prospective work in comparing syntax and semantics in written and spoken annotated corpora. 1
We report on a recently initiated project which aims at building a multi-layered parallel treebank of English and German. Particular attention is devoted to a dedicated predicate-argument layer which is used for aligning translationally equivalent sentences of the two languages. We describe both our conceptual decisions and aspects of their technical realisation. We discuss some selected problems and conclude with a few remarks on how this project relates to similar projects in the field.
A new public archive of norms, stimuli, data, and source code,www.psychonomic.org/archive, is at the service of researchers and students in experimental psychology. The archive has received contributions from more than 60 researchers. The August and November 2004 issues ofBehavior Research Methods, Instruments, & Computers comprise articles related to the inaugural contents of the archive, which will henceforth accept contributions related to articles published in all Psychonomic Society journals.
Style is embodied with different meanings in the eyes of different stylists. This paper concentrates on one of the views on style,deviation.Style is deviation of the norm which helps achieve the effect of foregrounding.Some specific examples are to show that foregrounding is prominence that is motivated.
Starting from the analysis of the verbal entries in the letter A of An Anglo-Saxon Dictionary (Bosworth and Toller 1973), this paper establishes the descriptive bases necessary for the elaboration of a lexical database of Old English. The basic terms (morphological process, derivational chain, inflexion, compounding, derivation, paradigm and root) are defined, the units (affixal and non-affixal predicates, primitive predicates and derived predicates) are classified and a methodology for the identification of the affixal predicates based on distribution and decomposition is put forward. Conclusions are drawn about the nature of derivational chains, the direction of derivation, and the distinction between inflexion and derivation, on the one hand, and compounding and derivation on the other.