Literatura académica sobre el tema "Generative lexicon"

Abstract This paper focuses on Semantic Roles, an important component of studies in lexical semantics, as they are captured as part of a bilingual (Czech-English) synonym lexicon called CzEngClass. This lexicon builds upon the existing valency lexicons included within the framework of the annotation of the various Prague Dependency Treebanks. The present analysis of Semantic Roles is being approached from the Functional Generative Description point of view and supported by the textual evidence taken specifically from the Prague Czech-English Dependency Treebank.

We consider Pustejovsky's account of the semantic lexicon. We discuss and reject his argument that the complexity of lexical entries is required to account for lexical generativity. Finally, we defend a sort of lexical atomism: though, strictly speaking, we concede that lexical entries are typically complex, still we claim that their complexity does not jeopardize either the thesis that lexical meaning is atomistic or the identification of lexical meaning with denotation.

<!--[if gte mso 10]> <mce:style><! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Vanlig tabell"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} > <! [endif] > <p class="MsoNormal"><span lang="EN-US">This paper discusses different types of zero-derived de-verbal nominals with a focus on result nominals, simple event nominals and complex event nominals. I argue that zero-derived nominals should be treated on a par with overtly derived nominals. I claim that verbs that have related zero-derived nominals have nominal gender features in their lexical entries in addition to verbal features, like Proc and Res, and that merging a gender feature on top of an event-structure representation results in a nominal. To capture the fact that verbal entries can be inserted in both nominal and verbal contexts, I apply the principle of underattachment, or underassociation, that allows lexical entries to be inserted in the syntax even when not all of the features in the lexical entry are present in the syntax (see e.g. Ramchand 2008 and Caha 2009). In verbal contexts, no gender feature is inserted, and in some of the nominal contexts, only a subset of the verb&rsquo;s event features are present. I further argue that the only function of overt nominalizing suffixes is to lexicalize a gender feature. If the lexical entry of a verb already contains a gender feature, no overt nominalizing suffix needs to be inserted. </span><-->

We propose a theory of the lexicon in which rules of grammar, encoded as declarative schemas, are lexical items containing variables. We develop a notation to encode precise relations among lexical items and show how this differs from the standard notion of inheritance. We also show how schemas can play both a generative role, acting as productive rules, and also a relational role, where they codify nonproductive but nevertheless prolific patterns within the lexicon. We then show how this theory of lexical relations can be embedded directly into a theory of lexical access and lexical processing, such that it can make direct contact with experimental findings.

In an attempt to provide a unified model of language-related mental processes, Jackendoff puts forward significant modifications to the generative architecture of the language faculty. While sympathetic to the overall objective of the book, my review points out that one aspect of the proposal – the status of the lexicon – lacks sufficient empirical support.

This thesis is the first of its kind to study the (linguistic) phenomenon of systematic polysemy and examine its pervasiveness in Arabic (both Modern Standard Arabic (MSA) and Jordanian Arabic (JA)). Systematic polysemy in this study is defined as the case where a lexeme has more than one distinct sense and the relationship between the senses is predictable by rules in language. In the narrow sense, however, this phenomenon refers only to the productive type of regular polysemy, which is defined vis-à-vis Apresjan’s (1974) notion of totality of scope (e.g. the content/container type). The integral function of this research is to (i) identify the major (as well as the minor) patterns of regular polysemy in Arabic in the major lexical categories of nouns, verbs, and adjectives; (ii) determine the extent to which these patterns converge with or diverge from the already explored patterns, mainly in English; and (iii) test the applicability of Pustejovsky’s (1995) Generative Lexicon (the GL) in accounting for the various Arabic data on polysemy. The study found that nearly every regular polysemous pattern observed in English was also present in Arabic, albeit with a few attested differences. For example, the regular pattern of the mass-to-count alternation (e.g. coffee—a coffee) is very rarely encountered in Arabic. In addition, the animal/meat alternation in English behaves rather differently in Arabic in the way the language elicits a non-countable (mass) meaning from a countable counterpart. With respect to lexicography, this study adds to the already studied patterns in Atkins and Rundell (2008). The dissertation also raises additional questions for the GL framework with respect to property nominalizations, nominalized adjectives, and generic collective nouns.

Cognitive Linguistics has been widely used to deal with the ambiguity generated by words in combination. Although this domain offers many solutions to address this challenge, not all of them can be implemented in a computational environment. The Dynamic Construal of Meaning framework is argued to have this ability because it describes an intrinsic degree of association of meanings, which in turn, can be translated into computational programs. A limitation towards a computational approach, however, has been the lack of syntactic parameters. This research argues that this limitation could be overcome with the aid of the Generative Lexicon Theory (GLT). Specifically, this dissertation formulated possible means to marry the GLT and Cognitive Linguistics in a novel rapprochement between the two. This bond between opposing theories provided the means to design a computational template (the AXEL System) by realising syntax and semantics at software levels. An instance of the AXEL system was created using a Design Research approach. Planned iterations were involved in the development to improve artefact performance. Such iterations boosted performance-improving, which accounted for the degree of association of meanings in three-noun compounds. This dissertation delivered three major contributions on the brink of a so-called turning point in Computational Linguistics (CL). First, the AXEL system was used to disclose hidden lexical patterns on ambiguity. These patterns are difficult, if not impossible, to be identified without automatic techniques. This research claimed that these patterns can assist audiences of linguists to review lexical knowledge on a software-based viewpoint. Following linguistic awareness, the second result advocated for the adoption of improved resources by decreasing electronic space of Sense Enumerative Lexicons (SELs). The AXEL system deployed the generation of “at the moment of use” interpretations, optimising the way the space is needed for lexical storage. Finally, this research introduced a subsystem of metrics to characterise an ambiguous degree of association of three-noun compounds enabling ranking methods. Weighing methods delivered mechanisms of classification of meanings towards Word Sense Disambiguation (WSD). Overall these results attempted to tackle difficulties in understanding studies of Lexical Semantics via software tools.

La información de clase semántica de los nombres es fundamental para una amplia variedad de tareas del procesamiento del lenguaje natural (PLN), como la traducción automática, la discriminación de referentes en tareas como la detección y el seguimiento de eventos, la búsqueda de respuestas, el reconocimiento y la clasificación de nombres de entidades, la construcción y ampliación automática de ontologías, la inferencia textual, etc. Una aproximación para resolver la construcción y el mantenimiento de los léxicos de gran cobertura que alimentan los sistemas de PNL, una tarea muy costosa y lenta, es la adquisición automática de información léxica, que consiste en la inducción de una clase semántica relacionada con una palabra en concreto a partir de datos de su distribución obtenidos de un corpus. Precisamente, por esta razón, se espera que la investigación actual sobre los métodos para la producción automática de léxicos de alta calidad, con gran cantidad de información y con anotación de clase como el trabajo que aquí presentamos, tenga un gran impacto en el rendimiento de la mayoría de las aplicaciones de PNL. En esta tesis, tratamos la adquisición automática de información léxica como un problema de clasificación. Con este propósito, adoptamos métodos de aprendizaje automático para generar un modelo que represente los datos de distribución vectorial que, basados en ejemplos conocidos, permitan hacer predicciones de otras palabras desconocidas. Las principales preguntas de investigación que planteamos en esta tesis son: (i) si los datos de corpus proporcionan suficiente información para construir representaciones de palabras de forma eficiente y que resulten en decisiones de clasificación precisas y sólidas, y (ii) si la adquisición automática puede gestionar, también, los nombres polisémicos. Para hacer frente a estos problemas, realizamos una serie de validaciones empíricas sobre nombres en inglés. Nuestros resultados confirman que la información obtenida a partir de la distribución de los datos de corpus es suficiente para adquirir automáticamente clases semánticas, como lo demuestra un valor-F global promedio de 0,80 aproximadamente utilizando varios modelos de recuento de contextos y en datos de corpus de distintos tamaños. No obstante, tanto el estado de la cuestión como los experimentos que realizamos destacaron una serie de retos para este tipo de modelos, que son reducir la escasez de datos del vector y dar cuenta de la polisemia nominal en las representaciones distribucionales de las palabras. En este contexto, los modelos de word embedding (WE) mantienen la “semántica” subyacente en las ocurrencias de un nombre en los datos de corpus asignándole un vector. Con esta elección, hemos sido capaces de superar el problema de la escasez de datos, como lo demuestra un valor-F general promedio de 0,91 para las clases semánticas de nombres de sentido único, a través de una combinación de la reducción de la dimensionalidad y de números reales. Además, las representaciones de WE obtuvieron un rendimiento superior en la gestión de las ocurrencias asimétricas de cada sentido de los nombres de tipo complejo polisémicos regulares en datos de corpus. Como resultado, hemos podido clasificar directamente esos nombres en su propia clase semántica con un valor-F global promedio de 0,85. La principal aportación de esta tesis consiste en una validación empírica de diferentes representaciones de distribución utilizadas para la clasificación semántica de nombres junto con una posterior expansión del trabajo anterior, lo que se traduce en recursos léxicos y conjuntos de datos innovadores que están disponibles de forma gratuita para su descarga y uso.
La información de clase semántica de los nombres es fundamental para una amplia variedad de tareas del procesamiento del lenguaje natural (PLN), como la traducción automática, la discriminación de referentes en tareas como la detección y el seguimiento de eventos, la búsqueda de respuestas, el reconocimiento y la clasificación de nombres de entidades, la construcción y ampliación automática de ontologías, la inferencia textual, etc. Una aproximación para resolver la construcción y el mantenimiento de los léxicos de gran cobertura que alimentan los sistemas de PNL, una tarea muy costosa y lenta, es la adquisición automática de información léxica, que consiste en la inducción de una clase semántica relacionada con una palabra en concreto a partir de datos de su distribución obtenidos de un corpus. Precisamente, por esta razón, se espera que la investigación actual sobre los métodos para la producción automática de léxicos de alta calidad, con gran cantidad de información y con anotación de clase como el trabajo que aquí presentamos, tenga un gran impacto en el rendimiento de la mayoría de las aplicaciones de PNL. En esta tesis, tratamos la adquisición automática de información léxica como un problema de clasificación. Con este propósito, adoptamos métodos de aprendizaje automático para generar un modelo que represente los datos de distribución vectorial que, basados en ejemplos conocidos, permitan hacer predicciones de otras palabras desconocidas. Las principales preguntas de investigación que planteamos en esta tesis son: (i) si los datos de corpus proporcionan suficiente información para construir representaciones de palabras de forma eficiente y que resulten en decisiones de clasificación precisas y sólidas, y (ii) si la adquisición automática puede gestionar, también, los nombres polisémicos. Para hacer frente a estos problemas, realizamos una serie de validaciones empíricas sobre nombres en inglés. Nuestros resultados confirman que la información obtenida a partir de la distribución de los datos de corpus es suficiente para adquirir automáticamente clases semánticas, como lo demuestra un valor-F global promedio de 0,80 aproximadamente utilizando varios modelos de recuento de contextos y en datos de corpus de distintos tamaños. No obstante, tanto el estado de la cuestión como los experimentos que realizamos destacaron una serie de retos para este tipo de modelos, que son reducir la escasez de datos del vector y dar cuenta de la polisemia nominal en las representaciones distribucionales de las palabras. En este contexto, los modelos de word embedding (WE) mantienen la “semántica” subyacente en las ocurrencias de un nombre en los datos de corpus asignándole un vector. Con esta elección, hemos sido capaces de superar el problema de la escasez de datos, como lo demuestra un valor-F general promedio de 0,91 para las clases semánticas de nombres de sentido único, a través de una combinación de la reducción de la dimensionalidad y de números reales. Además, las representaciones de WE obtuvieron un rendimiento superior en la gestión de las ocurrencias asimétricas de cada sentido de los nombres de tipo complejo polisémicos regulares en datos de corpus. Como resultado, hemos podido clasificar directamente esos nombres en su propia clase semántica con un valor-F global promedio de 0,85. La principal aportación de esta tesis consiste en una validación empírica de diferentes representaciones de distribución utilizadas para la clasificación semántica de nombres junto con una posterior expansión del trabajo anterior, lo que se traduce en recursos léxicos y conjuntos de datos innovadores que están disponibles de forma gratuita para su descarga y uso.
Lexical semantic class information for nouns is critical for a broad variety of Natural Language Processing (NLP) tasks including, but not limited to, machine translation, discrimination of referents in tasks such as event detection and tracking, question answering, named entity recognition and classification, automatic construction and extension of ontologies, textual inference, etc. One approach to solve the costly and time-consuming manual construction and maintenance of large-coverage lexica to feed NLP systems is the Automatic Acquisition of Lexical Information, which involves the induction of a semantic class related to a particular word from distributional data gathered within a corpus. This is precisely why current research on methods for the automatic production of high- quality information-rich class-annotated lexica, such as the work presented here, is expected to have a high impact on the performance of most NLP applications. In this thesis, we address the automatic acquisition of lexical information as a classification problem. For this reason, we adopt machine learning methods to generate a model representing vectorial distributional data which, grounded on known examples, allows for the predictions of other unknown words. The main research questions we investigate in this thesis are: (i) whether corpus data provides sufficient distributional information to build efficient word representations that result in accurate and robust classification decisions and (ii) whether automatic acquisition can handle also polysemous nouns. To tackle these problems, we conducted a number of empirical validations on English nouns. Our results confirmed that the distributional information obtained from corpus data is indeed sufficient to automatically acquire lexical semantic classes, demonstrated by an average overall F1-Score of almost 0.80 using diverse count-context models and on different sized corpus data. Nonetheless, both the State of the Art and the experiments we conducted highlighted a number of challenges of this type of model such as reducing vector sparsity and accounting for nominal polysemy in distributional word representations. In this context, Word Embeddings (WE) models maintain the “semantics” underlying the occurrences of a noun in corpus data by mapping it to a feature vector. With this choice, we were able to overcome the sparse data problem, demonstrated by an average overall F1-Score of 0.91 for single-sense lexical semantic noun classes, through a combination of reduced dimensionality and “real” numbers. In addition, the WE representations obtained a higher performance in handling the asymmetrical occurrences of each sense of regular polysemous complex-type nouns in corpus data. As a result, we were able to directly classify such nouns into their own lexical-semantic class with an average overall F1-Score of 0.85. The main contribution of this dissertation consists of an empirical validation of different distributional representations used for nominal lexical semantic classification along with a subsequent expansion of previous work, which results in novel lexical resources and data sets that have been made freely available for download and use.

Made available in DSpace on 2015-04-11T13:49:03Z (GMT). No. of bitstreams: 1 vanessa.pdf: 2538261 bytes, checksum: 22a79ec6ec4a9c95e3c0d9514975ea7d (MD5) Previous issue date: 2012-08-29
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
This study presents an analysis both syntactic and semantic of the verb poder in Brazilian Portuguese. To achieve this goal, we started with a literature review, which consisted of works dedicated to the study of auxiliarity and modality in order to determine what these issues imply and what is usually considered for classifying the verb under investigation as an auxiliary and/or modal verb. As foundations of this study, we used two theories, namely, HPSG (Head-Driven Phrase Structure Grammar Gramática de Estruturas Sintagmáticas Orientadas pelo Núcleo), a model of surface oriented generative grammar, which consists of a phonological, a syntactic and a semantic component, and GL (The Generative Lexicon O Léxico Gerativo), a lexicalist model of semantic interpretation of natural language, which is proposed to deal with problems such as compositionality, semantic creativity, and logical polysemy. Because these models are unable to handle the verb poder of the Brazilian Portuguese as they were originally proposed, it was necessary to use the GL to make some modifications in HPSG, in order to semantically enrich this model of grammar, so that it can cope with the logical polysemy of the verb poder, its behavior as a raising and a control verb, the saturation of its internal argument, as well as to identify when it is an auxiliary verb. The analysis showed that: (a) poder has four meanings inherent to it, namely, CAPACITY, ABILITY, POSSIBILITY and PERMISSION; (b) to saturate the internal argument of poder, the phrase candidate to saturate that argument must be of type [proposition] and the core of that phrase must be of type [event]. In case those types are not identical, the type coercion is applied in order to recover the requested type for that verb; (c) poder is a raising verb when it means POSSIBILITY, in such case it selects no external argument. That is, it accepts as its subject whatever the subject of its VP-complement is; (d) poder is a control verb when it means CAPACITY, ABILITY and/or PERMISSION and in this case it requires that the saturator of its internal argument be of type [entity] when poder means CAPACITY, or of type [animal] when it means ABILITY and/or PERMISSION; (e) poder is an auxiliary verb only when it is a raising verb, because only in this situation it does not impose any selectional restrictions on the external argument and (f ) poder is considered a modal verb because it can express an epistemic notion possibility and at least three non-epistemic notions of modality capacity, ability and permission.
Este trabalho apresenta uma análise tanto sintática quanto semântica do verbo poder do português brasileiro. Para alcançar esse objetivo, partiu-se de uma revisão de literatura, a qual compreendeu trabalhos dedicados ao estudo da auxiliaridade e da modalidade, a fim de verificar o que essas questões implicam e o que geralmente é levado em consideração para classificar o verbo investigado como auxiliar e/ou modal. Como alicerces deste trabalho, foram utilizadas duas teorias, quais sejam, a HPSG (Head-Driven Phrase Structure Grammar Gramática de Estruturas Sintagmáticas Orientadas pelo Núcleo), um modelo de gramática gerativa orientada pela superfície, a qual é constituída de um componente fonológico, um sintático e um semântico, e o GL (The Generative Lexicon O Léxico Gerativo), um modelo lexicalista de interpretação semântica de língua natural, que se propõe a lidar com problemas como a composicionalidade, a criatividade semântica e a polissemia lógica. Devido ao fato de esses modelos não conseguirem lidar com o verbo poder do português brasileiro como eles foram propostos originalmente, foi necessário utilizar o GL para fazer algumas modificações na HPSG, a fim de enriquecer semanticamente esse modelo de gramática, de modo que ele consiga dar conta da polissemia lógica do verbo poder, de seu comportamento como verbo de alçamento e de controle, da saturação de seu argumento interno, além de identificar quando ele é um verbo auxiliar. A análise mostrou que: (a) quatro são os significados inerentes ao verbo poder, quais sejam, CAPACIDADE, HABILIDADE, PERMISSÃO e POSSIBILIDADE; (b) para saturar o argumento interno do verbo poder, o sintagma candidato a saturador deve ser do tipo [proposição], e o núcleo desse sintagma deve ser do tipo [evento] e, não havendo essa identidade de tipos, recorre-se à aplicação da construção de coerção de tipo para recuperar o tipo solicitado por aquele verbo; (c) poder é verbo de alçamento quando significa POSSIBILIDADE e, nesse caso, não seleciona argumento externo. Ou seja, aceita como sujeito qualquer que seja o sujeito de seu VP-complemento; (d) poder é verbo de controle quando significa CAPACIDADE, HABILIDADE e/ou PERMISSÃO e, nesse caso, requer que o sintagma saturador de seu argumento interno seja ou do tipo [entidade], quando significa CAPACIDADE, ou do tipo [animal], quando significa HABILIDADE e/ou PERMISSÃO; (e) poder só é verbo auxiliar quando é um verbo de alçamento, pois só nessa situação não impõe restrições selecionais quanto ao argumento externo; e (f) poder é considerado um verbo modal porque pode expressar uma noção epistêmica possibilidade e pelo menos três noções não epistêmicas de modalidade capacidade, habilidade e permissão.

Thesis (MA (African Languages))--University of Stellenbosch, 2007.
This study explores semantic analysis of motion verb-hamba in IsiXhosa. In chapter 1 I have stated the aim of the study. I have discussed properties related to the lexical semantic analysis of the verb-hamba as well as Pustejovsky’s theory of the Generative Lexicon. The theoretical framework and the organization of study are also discussed in this chapter. Chapter 2 addresses in more detail the type system for semantics. A generative theory of the lexicon includes multiple levels of representation for different types of lexical information needed. These levels include Argument Structure, Event Structure, Qualia Structure and Lexical Inherent Structure. In this chapter there is a more detailed structure of the qualia and the role they play in distributing the functional behavior of words and phrases in composition. In chapter 3 I have examined the lexical semantic analysis of the verb-hamba to account for the range of selectional properties of the NP phrase subject argument of the verb-hamba and various interpretations that arise in terms of composition with its complement arguments. The polysemous behavior of the verb-hamba is examined in sentence alternation constructions with respect to the properties of the event structure. I have also investigated the lexical representation in terms of argument structure and the event structure of the verb-hamba in different sentences. Chapter 4 is the conclusion, summarizing the findings of all the previous chapters in this study on lexical semantic analysis of the motion verb-hamba in IsiXhosa. This is followed by word lists that contain meanings of words in the context in which they are used.

This work proposes an automatic system of analysis for Organic Chemistry compound names aiming to generate pictures of chemical structures. In order to accomplish this, the system receives an organic compound name, analyzes it syntactically and semantically and, if it represents a correct chemical compound, generates a visual output for the corresponding structure. An advance that the system shows in relation to other systems which deal with the same problem consists on being able to analyse both compound names that satisfy the current o±cial nomenclature constraints and those that, despite of do not respect them, represent correct organic compounds (in this case, the system will have solved a nomenclature ambiguity problem). The semantic and syntactic analysis are guided by the name component types of the chemical compounds, which motivated an implementation that fits into the Generative Lexicon Theory (GLT) formalism. Furthermore, the analysis guided by types justified the decision of implementing the system by using the parser combinators and the Clean Funcional Language as very adequate and eficient tools. The implemented system represents a significative utilitarian as an automatic Organic Chemistry instructor.
O presente trabalho propõe um sistema automático de análise de nomes de compostos da Química Orgânica visando a geração do desenho de suas estruturas químicas. Para tanto, o sistema recebe um nome de um composto orgânico, analisa-o sintática e semanticamente e, caso ele represente um composto quimicamente correto, gera uma saída visual para a estrutura química que lhe corresponda. Um avanço que o sistema apresenta com relação a outros que se propõem a efetuar tarefa semelhante e o fato de ele conseguir analisar tanto nomes de compostos que se enquadram nos padrões das nomenclaturas oficiais vigentes, quanto aqueles que, apesar de não se enquadrarem nos mesmos, representam compostos orgânicos verdadeiros (quando ocorrer tal situação, o sistema teria resolvido um problema de ambigüidade de nomenclatura). As análises sintática e semântica são guiadas pelos tipos dos componentes dos nomes químicos, fato que motivou a implementação do sistema nos moldes do formalismo da Teoria do Léxico Gerativo (TLG). Além disso, as análises guiadas pelo tipo motivaram a escolha dos combinadores de Parser e da Linguagem de Programação Funcional Clean como utilitários eficazes e adequados na execução das análises lingüísticas. O sistema implementado representa uma ferramenta muito útil como instrutor automático de Química Orgânica.
Mestre em Ciência da Computação

Thesis (MA (African Languages))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: The main concern of this thesis relates to an investigation of the lexical-semantic nature of the motion verb –phuma (exit, go out) in isiZulu within the framework of Generative Lexicon Theory. In particular, the thesis explores the event structure and aspectual verb class properties in the locative-subject alternation with the verb –phuma in isiZulu. Chapter one presents a general introduction to the study, stating the purpose and aims of the research, giving a broad perspective of the theoretical framework adopted, and outlining the organisation of the investigation of the lexical-semantic properties of –phuma. Chapter two presents a detailed discussion of Generative Lexicon Theory, which centrally concerns accounting for polysemy phenomena across various nominal and verbal expressions. The four dimensions of lexical-semantic representation that constitute the central theoretical properties in Generative Lexicon Theory are reviewed, i.e. Argument structure, Event structure, Qualia structure and Lexical Inheritance structure. In addition, the various facets of meaning of Qualia structure namely Fomral, Constitutive, Telic and Agentive facets, are described in relation to their theoretical significance in accounting for word meaning and polysemy. Chapter three examines in a systematic and comprehensive way the range of locative-subject alternation possibilities with the verb –phuma. In particular the range of semantic types of the NP subject argument of –phuma taking a locative complement is explored to determine whether all these sentences permit a corresponding locative-alternation construction. In addition, the aspectual verb class properties of the two variants in the alternation are analysed with regard to a range of diagnositics associated with stative events, activity events, achievement events and accomplishments events. It is known that the two variants in the alternation can be distinguished in terms of their aspectual verb class properties. Chapter four summarises the main findings of the study and presents the conclusion.
AFRIKAANSE OPSOMMING: Die hoofbelang van hierdie tesis hou verband met die ondersoek van die leksikaal-semantiese aard van die bewegingswerkwoorde –phuma in isiZulu binne die raamwerk van Generatiewe Leksikon teorie soos uiteengesit deur Pustejovsky (1996). Die tesis ondersoek spesifiek die gebeurtenis ('event') struktuur en aspektuele werkwoordklas eienskappe in die lokatief-subjek alternasie met die werkwoord –phuma in isiZulu. Hoofstuk een gee 'n algemene oorsig van die studie, stel die doelstellings van die teoretiese raamwerk wat aanvaar word, en skets die organisasie van die studie oor die leksikaalsemantiese kenmerke van –phuma. Hoofstuk twee bied 'n detail bespreking van Generatiewe Leksikonteorie, wat sentraal verband hou met die verklaring van polisemieverskynsels van naamwoordelike en werkwoordelike uitdrukkings. Die vier dimensies van leksikaal-semantiese representasie wat die sentrale teoretiese eienskappe vorm in Generatiewe Leksikonteorie word beskou, naamlik argumentstruktuur, Gebeurtenis ('Event') struktuur, Qualiastruktuur en Leksikaleerwingstruktuur. Voorts word die verskillende fasette van betekenis van Qualiastruktuur, nl. Formeel, Konstitief, Doel ('Telic') en Agentief beskryf rakende die teoretiese belang daarvan vir die verklaring van woordbetekenis en polisemie. Hoofstuk drie ondersoek op 'n sistematiese wyse die verskeidenheid van lokatief-subjek alternasie moontlikhede met die werkwoord –phuma. In die besonder, word die semantiese tipes van die NP subjek argument van –phuma wat 'n lokatiewe komplement neem ondersoek om te bepaal watter van hierdie sinne neem 'n lokatiewe-alternasie konstruksie. Voorts word die aspektuele werkwoordklas kenmerke van die twee variante in die alternasie ontleed met verwysing na 'n reeks toetse vir die onderskeid van aspektuele werkwoordklasse. Daar word aangetoon dat die twee alternasies onderskei kan word in terme van aspektuele werkwoordklas. Hoofstuk vier gee die opsomming en konklusie van die studie.
UKUBUKEZA KAFUSHANE: Lesi sifundo sibheka ucwaningo lwesimantikhi yelekhizikhoni yezenzo ezikhethiweyo esiZulwini. Esahlukweni soku – 1, injongo yalesisifundo iyashiwo, imiphumela yocwaningo mayelana nesimathikhi yelekhizikhoni yesenzo u – phuma kanjalo nengqikithi yelekhizikhoni itshengiswe ngokukaPustejovosky (1996). Isimo sengqikithi kanye nokulungiselelwa kwesifundo kuzoxoxwa ngakho kulesisifundo. Isahluko sesi – 2 siveza uhlobo lwesimantikhi yethu. Ulwazi olucutshunguliwe lwelekhizikhoni lufaka amazinga amaningi amele izinhlobo ezahlukeneyo zolwazi lwesimantikhi. Kula mazinga singabala isakhiwo sempikiswano, isakhiwo sesigameko, isakhiwo sekhwaliya kanye nesakhiwo esisohlwini ololandelayo. Lesi sahluko sesibili sibuye siboniso ngokucace kakhulu ngokwesakhiwo sekhwaliya nangendima edlaliwe ekuqhubekiseni imisebenzi yamagama kanye namabinzana ahlanganisiwe. Isahluko sesi – 3 sihlola ucwaningo lwesimantikhi lwesenzo u – phuma esimayelana nezingxenye zezimpawu ezikhethiweyo zempikiswano yebinzana lebizo eliyinhloko yesenzo u – phuma kanye nezincazelo ezahiukahlukene ezivela emagameni ahlanganiswe ngokwempikiswano yemfezeko. Izindlela zezincazelo eziningi zesenzo u – phuma zihloliwe esakhiweni sokushintshana emishweni ngokubandakanye esakhiweni sesigameko. Incazelo yelekhizikhoni ngokwamagama esakhiwo sempikiswano kanye nesakhiwo sesigameko sesenzo u – phuma emishweni eyahlukahlukene icutshunguliwe. Isahluko sesi – 4 siyisiphetho esifingqa konke okutholakala ezahlukweni ezindlule esifundweni socwaningo lwelekhizikhoni yesimantikhi yezenzo ezikhethwe esiZulwini.

Le problème de représentation automatique de la signification logique des énoncés ambigus en langage naturel a suscité l'intérêt des chercheurs dans le domaine de la sémantique computationnelle et de la logique. L'ambiguïté dans le langage naturel peut se manifester au niveau lexical / syntaxique / sémantique de la construction de sens, ou elle peut être causée par d'autres facteurs tels que la grammaticalité et le manque de contexte dans lequel la phrase est effectivement prononcée. L'approche traditionnelle Montagovienne ainsi que ses extensions modernes ont tenté de capturer ce phénomène en fournissant quelques modèles qui permettent la génération automatique de formules logiques. Cependant, il existe un axe de recherche qui n'est pas encore profondément étudié: classer les interprétations d'énoncés ambigus en fonction des préférences réelles des utilisateurs de la langue. Ce manque suggère une nouvelle direction d'étude qui est partiellement explorée dans ce mémoire en modélisant des préférences de sens en alignement avec certaines des théories de performance préférentielles humaines bien étudiées disponibles dans la littérature linguistique et psycholinguistique.Afin d'atteindre cet objectif, nous suggérons d'utiliser / d'étendre les Grammaires catégorielles pour notre analyse syntaxique et les Réseaux catégoriels de preuve comme notre analyse syntaxique. Nous utilisons également le Lexique Génératif Montagovien pour dériver une formule logique multi-triée comme notre représentation de signification sémantique. Cela ouvrirait la voie à nos contributions à cinq volets, à savoir, (i) le classement de la portée du quantificateur multiple au moyen de l'opérateur epsilon de Hilbert sous-spécifié et des réseaux de preuve catégoriels; (ii) modéliser la gradation sémantique dans les phrases qui ont des coercitions implicites dans leurs significations. Nous utilisons un cadre appelé Montagovian Generative Lexicon. Notre tâche est d'introduire une procédure pour incorporer des types et des coercitions en utilisant des données lexicales produites par externalisation ouverte qui sont recueillies par un jeu sérieux appelé JeuxDeMots; (iii) l'introduction de nouvelles métriques sensibles au référent basées sur la localité pour mesurer la complexité linguistique au moyen de réseaux de preuve catégoriels; (iv) l'introduction d'algorithmes pour l'achèvement des phrases avec différentes mesures linguistiquement motivées pour sélectionner les meilleurs candidats; (v)l'intégration de différentes métriques de calcul pour les préférences de classement afin de faire d'elles un modèle unique
The problem of automatic logical meaning representation for ambiguous natural language utterances has been the subject of interest among the researchers in the domain of computational and logical semantics. Ambiguity in natural language may be caused in lexical/syntactical/semantical level of the meaning construction or it may be caused by other factors such as ungrammaticality and lack of the context in which the sentence is actually uttered. The traditional Montagovian framework and the family of its modern extensions have tried to capture this phenomenon by providing some models that enable the automatic generation of logical formulas as the meaning representation. However, there is a line of research which is not profoundly investigated yet: to rank the interpretations of ambiguous utterances based on the real preferences of the language users. This gap suggests a new direction for study which is partially carried out in this dissertation by modeling meaning preferences in alignment with some of the well-studied human preferential performance theories available in the linguistics and psycholinguistics literature.In order to fulfill this goal, we suggest to use/extend Categorial Grammars for our syntactical analysis and Categorial Proof Nets as our syntactic parse. We also use Montagovian Generative Lexicon for deriving multi-sorted logical formula as our semantical meaning representation. This would pave the way for our five-folded contributions, namely, (i) ranking the multiple-quantifier scoping by means of underspecified Hilbert's epsilon operator and categorial proof nets; (ii) modeling the semantic gradience in sentences that have implicit coercions in their meanings. We use a framework called Montagovian Generative Lexicon. Our task is introducing a procedure for incorporating types and coercions using crowd-sourced lexical data that is gathered by a serious game called JeuxDeMots; (iii) introducing a new locality-based referent-sensitive metrics for measuring linguistic complexity by means of Categorial Proof Nets; (iv) introducing algorithms for sentence completions with different linguistically motivated metrics to select the best candidates; (v) and finally integration of different computational metrics for ranking preferences in order to make them a unique model

El objetivo de esta tesis, es reunir información gramatical suficiente que permita determinar qué características deben reunir las bases verbales del español para ser capaces de originar un adjetivo deverbal de participio (ADP), y, basados en ello, poder describir cómo opera el proceso de activación de valor especializado en los términos N+ADP del dominio de la economía. Estas construcciones sintácticas mínimas son muy productivas en los discursos de ámbitos especializados, sin embargo la naturaleza híbrida de la forma participial acarrea muchos conflictos a la tarea de Procesamiento de Lenguaje Natural (PNL). Esta aproximación al análisis de los ADP es lingüística, está anclada teóricamente en la Teoría Comunicativa de la Terminología (TCT) e intenta ser el punto de contacto entre teoría y aplicación.
The goal of this thesis is to pinpoint the grammatical information that is necessary to determine which Spanish verb stems give rise to an adjectival participle (AP). This information will allow us to describe the linguistic indicators that, within the domain of economy, activate a specialized meaning in those terms that have the structure AP+noun. These syntactic minimal constructions are highly productive in specialized discourse. Nevertheless, the hybrid nature of the participial form invokes many conflicts in Natural Language Processing (NLP) applications. This descriptive approach to the adjectival participles is linguistic in nature, based on the Communicative Theory of Terminology (CTT), intends to be the point of contact between theory and application.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-05T16:40:06Z No. of bitstreams: 1 elyedisondasilvamatos.pdf: 5520917 bytes, checksum: c9e7d798d96928a6ad4f2ee48d912531 (MD5)
Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-26T11:51:46Z (GMT) No. of bitstreams: 1 elyedisondasilvamatos.pdf: 5520917 bytes, checksum: c9e7d798d96928a6ad4f2ee48d912531 (MD5)
Made available in DSpace on 2016-02-26T11:51:47Z (GMT). No. of bitstreams: 1 elyedisondasilvamatos.pdf: 5520917 bytes, checksum: c9e7d798d96928a6ad4f2ee48d912531 (MD5) Previous issue date: 2014-06-27
Enquanto no âmbito da Sintaxe, as técnicas, os algoritmos e as aplicações em Processamento da Língua Natural são bem estudados e já estão relativamente bem estabelecidos, no âmbito da Semântica não é possível observar ainda a mesma maturidade. Visando, então, contribuir para os estudos em Semântica Computacional, este trabalho busca maneiras de implementar algumas das ideias e dos insights propostos pela Linguística Cognitiva, que é, por si, uma alternativa à Linguística Gerativa. A tentativa é reunir algumas das ferramentas disponíveis, seja no viés computacional (Bancos de Dados, Teoria dos Grafos, Ontologias, Mecanismos de inferências, Modelos Conexionistas), seja no viés linguístico (Semântica de Frames e Teoria do Léxico Gerativo), seja no viés de aplicações (FrameNet e ontologia SIMPLE), a fim de abordar as questões semânticas de forma mais flexível. O objeto de estudo é o processo de desambiguação de Unidades Lexicais. O resultado da pesquisa realizada é corporificado na forma de uma aplicação computacional, chamada Framework LUDI (Lexical Unit Discovery through Inference), composta por algoritmos e estruturas de dados usados na desambiguação. O framework é uma aplicação de Compreensão da Língua Natural, que pode ser integrada em ferramentas para recuperação de informação e sumarização, bem como em processos de Etiquetagem de Papéis Semânticos (SRL - Semantic Role Labeling).
While in the field of Syntax techniques, algorithms and applications in Natural Language Processing are well known and relatively well established, the same situation does not hold for the field of Semantics. Aiming at contributing to the studies in Computational Semantics, this work implements ideas and insights offered by Cognitive Linguistics, which is itself an alternative to Generative Linguistics. We attempt to bring together contributions from the computational domain (Databases, Graph Theory, Ontologies, inference mechanisms, Connectionists Models), the linguistic domain (Frame Semantics and the Generative Lexicon), and the application domain (FrameNet and SIMPLE Ontology) in order to address the semantic issues more flexibly. The object of study is the process of disambiguation of Lexical Units. The results of the research are embodied in the form of a computer application, called Framework LUDI (Lexical Unit Discovery through Inference), and composed of algorithms and data structures used for Lexical Unit disambiguation. The framework is an application of Natural Language Understanding, which can be integrated into information retrieval and summarization tools, as well as into processes of Semantic Role Labeling (SRL).

Opinion mining is a popular task, that is applied, for example, to determine news polarisation and identify product review classes. Our task is unsupervised clusterization of opinionated texts, in particular news on political events. Many papers that tackle this issue use generative models based on lexical features. Our goal is to determine the entities defying an opinion amongst lexical, syntactic and semantic features as well as their compositions. More specifically, we test the hypothesis that an opinion is determined by the composition of the mentioned facts (SPO triples), the semantic roles of the words and the sentiment lexicon used in it. In this paper we formalise this task and prove that using a composition of the above features provides the best quality when clusterising opinionated texts. To test this hypothesis we have gathered and labelled two corpuses of news on political events and proposed a set of unsupervised algorithms for extracting the features.

Designers bring individual knowledge and perspectives to the team. The hypothesis tested in this research is that semantic and grammatical structures (the language through which concepts are expressed) enable designers to bridge relations among ideas stored in each designer’s mind and from this to generate design concepts. This paper describes a linguistic and a computational method to examine the grammatical and semantic structure of design conversations and the linguistic processes by which individuals bridge their knowledge to the group’s ongoing knowledge accumulation. To test the hypothesis, we conducted a linguistic (systemic functional linguistics) and computational linguistic (lexical chain analysis) analysis of a design team conversation The computational analysis revealed hypernym relations as the primary lexico-syntactic pattern by which designers offer, interrelate and develop concepts. The linguistic analysis highlighted the grammatical linguistic features that actively contribute to the generation of design content by teams. These analyses point to the prospect of a functional correspondence between language use and a team’s ability to construct knowledge for design. This interrelation has implications both for computational systems that assess design teams and design teamwork education.

Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems. So far, research has mostly focused on English language, for other languages both standardized datasets, as well as experiments with state-of-the-art models, are rare. In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial networks, which we trained on the new dataset. We evaluate the generated text regarding metrics such as perplexity, grammatical correctness and lexical diversity.