La traduction des Entités Nommées (EN) est un axe de recherche d'actualité vu la multitude des documents électroniques échangés à travers Internet. Ainsi, le besoin de traiter ces documents par des outils de TALN est devenu nécessaire et intéressant. La modélisation formelle ou semi formelle de ces EN peut intervenir dans les processus de reconnaissance et de traduction. En effet, elle permet de rendre plus fiable la constitution des ressources linquistiques, de limiter l'impact des spécificités linguistiques ct de faciliter les transformations d'une représentation à une autre. Dans ce contexte, nous proposons un outil de reconnaissance ct de traduction vers le français des EN arabes basé essentiellement sur une représentation formelle et sur un ensemble de transducteurs. L'outil prend en compte l'intégration d'un module de translittération. L'implémentation de cet outil a été effectuée en utilisant la plateforme NooJ. Les résultats obtenus sont satisfaisants / The translation of named entities (NEs) is a current research topic with regard to the proliferation of electronic documents exchanged through the Internet. So, the need to process these documents with NLP tools becomes necessary and interesting. Formal or semi-formal modeling of these NEs may intervene in both processes of recognition and translation. Indeed, it makes the accumulation of linguistic resources more reliable, limits the impact of linguistic specificities and facilitates the transformation from one representation to another. In this context, we propose a tool for the recognition and translation of Arabic NEs into French, based primarily on formal .representation and a set of transducers. This tool takes into account the integration of a module of transliteration. Its implementation was performed using the NooJ platform and the results obtained proved to be satisfactory

La thèse a deux objectifs : le premier est de développer un analyseur qui permet d'analyser automatiquement des sources textuelles en chinois simplifié afin de segmenter les textes en mots et de les étiqueter par catégories grammaticales, ainsi que de construire les relations syntaxiques entre les mots. Le deuxième est d'extraire des informations autour des entités et des actions qui nous intéressent à partir des textes analysés. Afin d'atteindre ces deux objectifs, nous avons traité principalement les problématiques suivantes : les ambiguïtés de segmentation, la catégorisation ; le traitement des mots inconnus dans les textes chinois ; l'ambiguïté de l'analyse syntaxique ; la reconnaissance et le typage des entités nommées. Le texte d'entrée est traité phrase par phrase. L'analyseur commence par un traitement typographique au sein des phrases afin d'identifier les écritures latines et les chiffres. Ensuite, nous segmentons la phrase en mots à l'aide de dictionnaires. Grâce aux règles linguistiques, nous créons des hypothèses de noms propres, changeons les poids des catégories ou des mots selon leur contextes gauches ou/et droits. Un modèle de langue n-gramme élaboré à partir d'un corpus d'apprentissage permet de sélectionner le meilleur résultat de segmentation et de catégorisation. Une analyse en dépendance est utilisée pour marquer les relations entre les mots. Nous effectuons une première identification d'entités nommées à la fin de l'analyse syntaxique. Ceci permet d'identifier les entités nommées en unité ou en groupe nominal et également de leur attribuer un type. Ces entités nommées sont ensuite utilisées dans l'extraction. Les règles d'extraction permettent de valider ou de changer les types des entités nommées. L'extraction des connaissances est composée des deux étapes : extraire et annoter automatiquement des contenus à partir des textes analysés ; vérifier les contenus extraits et résoudre la cohérence à travers une ontologie. / We have developed an automatic analyser and an extraction module for Chinese langage processing. The analyser performs automatic Chinese word segmentation based on linguistic rules and dictionaries, part-of-speech tagging based on n-gram statistics and dependency grammar parsing. The module allows to extract information around named entities and activities. In order to achieve these goals, we have tackled the following main issues: segmentation and part-of-speech ambiguity; unknown word identification in Chinese text; attachment ambiguity in parsing. Chinese texts are analysed sentence by sentence. Given a sentence, the analyzer begins with typographic processing to identify sequences of Latin characters and numbers. Then, dictionaries are used for preliminary segmentation into words. Linguistic-based rules are used to create proper noun hypotheses and change the weight of some word categories. These rules take into account word context. An n-gram language model is created from a training corpus and selects the best word segmentation and parts-of-speech. Dependency grammar parsing is used to annotate relations between words. A first step of named entity recognition is performed after parsing. Its goal is to identify single-word named entities and noun-phrase-based named entities and to determine their semantic type. These named entities are then used in knowledge extraction. Knowledge extraction rules are used to validate named entities or to change their types. Knowledge extraction consists of two steps: automatic content extraction and tagging from analysed text; extracted contents control and ontology-based co-reference resolution.

La recherche de réponses précises à des questions formulées en langue naturelle renouvelle le champ de la recherche d’information. De nombreux travaux ont eu lieu sur la recherche de réponses à des questions factuelles en domaine ouvert. Moins de travaux ont porté sur la recherche de réponses en domaine de spécialité, en particulier dans le domaine médical ou biomédical. Plusieurs conditions différentes sont rencontrées en domaine de spécialité comme les lexiques et terminologies spécialisés, les types particuliers de questions, entités et relations du domaine ou les caractéristiques des documents ciblés. Dans une première partie, nous étudions les méthodes permettant d’analyser sémantiquement les questions posées par l’utilisateur ainsi que les textes utilisés pour trouver les réponses. Pour ce faire nous utilisons des méthodes hybrides pour deux tâches principales : (i) la reconnaissance des entités médicales et (ii) l’extraction de relations sémantiques. Ces méthodes combinent des règles et patrons construits manuellement, des connaissances du domaine et des techniques d’apprentissage statistique utilisant différents classifieurs. Ces méthodes hybrides, expérimentées sur différents corpus, permettent de pallier les inconvénients des deux types de méthodes d’extraction d’information, à savoir le manque de couverture potentiel des méthodes à base de règles et la dépendance aux données annotées des méthodes statistiques. Dans une seconde partie, nous étudions l’apport des technologies du web sémantique pour la portabilité et l’expressivité des systèmes de questions-réponses. Dans le cadre de notre approche, nous exploitons les technologies du web sémantique pour annoter les informations extraites en premier lieu et pour interroger sémantiquement ces annotations en second lieu. Enfin, nous présentons notre système de questions-réponses, appelé MEANS, qui utilise à la fois des techniques de TAL, des connaissances du domaine et les technologies du web sémantique pour répondre automatiquement aux questions médicales. / With the dramatic growth of digital information, finding precise answers to natural language questions is more and more essential for retrieving domain knowledge in real time. Many research works tackled answer retrieval for factual questions in open domain. Less works were performed for domain-specific question answering such as the medical domain. Compared to the open domain, several different conditions are met in the medical domain such as specialized vocabularies, specific types of questions, different kinds of domain entities and relations. Document characteristics are also a matter of importance, as, for example, clinical texts may tend to use a lot of technical abbreviations while forum pages may use long “approximate” terms. We focus on finding precise answers to natural language questions in the medical field. A key process for this task is to analyze the questions and the source documents semantically and to use standard formalisms to represent the obtained annotations. We propose a medical question-answering approach based on: (i) NLP methods combing domain knowledge, rule-based methods and statistical ones to extract relevant information from questions and documents and (ii) Semantic Web technologies to represent and interrogate the extracted information.

La traduction automatique des documents est considérée comme l’une des tâches les plus difficiles en traitement automatique des langues et de la parole. Les particularités linguistiques de certaines langues, comme la langue arabe, rendent la tâche de traduction automatique plus difficile. Notre objectif dans cette thèse est d'améliorer les systèmes de traduction de l'arabe vers le français et vers l'anglais. Nous proposons donc une étude détaillée sur ces systèmes. Les principales recherches portent à la fois sur la construction de corpus parallèles, le prétraitement de l'arabe et sur l'adaptation des modèles de traduction et de langue.Tout d'abord, un corpus comparable journalistique a été exploré pour en extraire automatiquement un corpus parallèle. Ensuite, différentes approches d’adaptation du modèle de traduction sont exploitées, soit en utilisant le corpus parallèle extrait automatiquement soit en utilisant un corpus parallèle construit automatiquement.Nous démontrons que l'adaptation des données du système de traduction permet d'améliorer la traduction. Un texte en arabe doit être prétraité avant de le traduire et ceci à cause du caractère agglutinatif de la langue arabe. Nous présentons notre outil de segmentation de l'arabe, SAPA (Segmentor and Part-of-speech tagger for Arabic), indépendant de toute ressource externe et permettant de réduire les temps de calcul. Cet outil permet de prédire simultanément l’étiquette morpho-syntaxique ainsi que les proclitiques (conjonctions, prépositions, etc.) pour chaque mot, ensuite de séparer les proclitiques du lemme (ou mot de base). Nous décrivons également dans cette thèse notre outil de détection des entités nommées, NERAr (Named Entity Recognition for Arabic), et nous examions l'impact de l'intégration de la détection des entités nommées dans la tâche de prétraitement et la pré-traduction de ces entités nommées en utilisant des dictionnaires bilingues. Nous présentons par la suite plusieurs méthodes pour l'adaptation thématique des modèles de traduction et de langue expérimentées sur une application réelle contenant un corpus constitué d’un ensemble de phrases multicatégoriques.Ces expériences ouvrent des perspectives importantes de recherche comme par exemple la combinaison de plusieurs systèmes lors de la traduction pour l'adaptation thématique. Il serait également intéressant d'effectuer une adaptation temporelle des modèles de traduction et de langue. Finalement, les systèmes de traduction améliorés arabe-français et arabe-anglais sont intégrés dans une plateforme d'analyse multimédia et montrent une amélioration des performances par rapport aux systèmes de traduction de base. / Machine Translation is one of the most difficult tasks in natural language and speech processing. The linguistic peculiarities of some languages makes the machine translation task more difficult. In this thesis, we present a detailed study of machine translation systems from arabic to french and to english.Our principle researches carry on building parallel corpora, arabic preprocessing and adapting translation and language models. We propose a method for automatic extraction of parallel news corpora from a comparable corpora. Two approaches for translation model adaptation are explored using whether parallel corpora extracted automatically or parallel corpora constructed automatically. We demonstrate that adapting data used to build machine translation system improves translation.Arabic texts have to be preprocessed before machine translation and this because of the agglutinative character of arabic language. A prepocessing tool for arabic, SAPA (Segmentor and Part-of-speech tagger for Arabic), much faster than the state of the art tools and totally independant of any other external resource was developed. This tool predicts simultaneously morphosyntactic tags and proclitics (conjunctions, prepositions, etc.) for every word, then splits off words into lemma and proclitics.We describe also in this thesis, our named entity recognition tool for arabic, NERAr, and we focus on the impact of integrating named entity recognition in the preprocessing task. We used bilingual dictionaries to propose translations of the detected named entities. We present then many approaches to adapt thematically translation and language models using a corpora consists of a set of multicategoric sentences.These experiments open important research perspectives such as combining many systems when translating. It would be interesting also to focus on a temporal adaptation of translation and language models.Finally, improved machine translation systems from arabic to french and english are integrated in a multimedia platform analysis and shows improvements compared to basic machine translation systems.

A partir do final do século 20, o governo federal criou vários programas visando a ampliação de acesso ao saneamento básico. Embora esses programas tenham trazido o abastecimento de água potável e a coleta de resíduos sólidos para a maioria dos municípios brasileiros, o esgotamento sanitário ainda está espacialmente concentrado na região Sudeste e nas áreas mais urbanizadas. Para explicar esse padrão espacialmente concentrado, é frequentemente assumido que o tamanho das cidades realmente importa para o saneamento básico, especialmente para o esgotamento sanitário. De fato, à medida que as cidades crescem em tamanho, devemos esperar economias de escala no volume de infraestrutura de saneamento. Economias de escala na infra-estrutura implicam uma redução nos custos de saneamento básico, de forma proporcional ao tamanho da cidade, levando também a uma (esperada) relação de lei de escala (ou de potência) entre os gastos com saneamento básico e o tamanho da cidade. Usando a população, N(t), como medida do tamanho da cidade no momento t, a lei de escala para infraestrutura assume o formato Y(t) = Y0N(t)&#946 onde &#946 &#8776 0.8 < 1, Y denota o volume de infraestrutura e Y0 é uma constante. Diversas propriedades das cidades, desde a produção de patentes e renda até a extensão da rede elétrica, são funções de lei de potência do tamanho da população com expoentes de escalamento, &#946, que se enquadram em classes distintas. As quantidades que refletem a criação de riqueza e a inovação têm &#946 &#8776 1.2 > 1 (retornos crescentes), enquanto aquelas responsáveis pela infraestrutura exibem &#946 &#8776 0.8 < 1 (economias de escala). Verificamos essa relação com base em dados extraídos do Sistema Integrado de Planejamento e Orçamento (SIOP), que abrangem transferências com recursos não onerosos, previstos na Lei Orçamentária Anual (LOA), na modalidade saneamento básico. No conjunto, os valores estimados de &#946 mostram redução das transferências da União Federal para saneamento básico, de forma proporcional ao tamanho dos municípios beneficiários. Para a dotação inicial, valores programados na LOA, estimado foi de aproximadamente: 0.63 para municípios com população superior a dois mil habitantes; 0.92 para municípios acima de vinte mil habitantes; e 1.18 para municípios com mais de cinquenta mil habitantes. A segunda fonte de dados identificada foi o Diário Oficial da União (DOU), periódico do governo federal para publicação de atos oficiais. Os dados fornecidos pelo DOU referem-se aos recursos não onerosos e também aos empréstimos com recursos do Fundo de Garantia por Tempo de Serviço (FGTS). Para extração dos dados textuais foram utilizadas técnicas de Processamento de Linguagem Natural(PLN). Essas técnicas funcionam melhor quando os algoritmos são alimentados com anotações - metadados que fornecem informações adicionais sobre o texto. Por isso geramos uma base de dados, a partir de textos anotados do DOU, para treinar uma rede LSTM bidirecional aplicada à etiquetagem morfossintática e ao reconhecimento de entidades nomeadas. Os resultados preliminares obtidos dessa forma estão relatados no texto / Starting in the late 20th century, the Brazilian federal government created several programs to increase the access to water and sanitation. However, although these programs made improvements in water access, sanitation was generally overlooked. While water supply, and waste collection are available in the majority of the Brazilian municipalities, the sewage system is still spatially concentrated in the Southeast region and in the most urbanized areas. In order to explain this spatially concentrated pattern it is frequently assumed that the size of cities does really matter for sanitation services provision, specially for sewage collection. As a matter of fact, as cities grow in size, one should expect economies of scale in sanitation infrastructure volume. Economies of scale in sanitation infrastructure means a decrease in basic sanitation costs, proportional to the city size, leading also to a (expected) power law relationship between the expenditure on sanitation and city size.Using population, N(t), as the measure of city size at time t, power law scaling for infrastructure takes the form Y(t) = Y0N(t)&#946 where &#946 &#8776 0.8 < 1, Y denotes infrastructure volume and is a constant. Many diverse properties of cities from patent production and personal income to electrical cable length are shown to be power law functions of population size with scaling exponents, &#946, that fall into distinct universality classes. Quantities reflecting wealth creation and innovation have &#946 &#8776 1.2 > 1 (increasing returns), whereas those accounting for infrastructure display &#946 &#8776 0.8 < 1 (economies of scale). We verified this relationship using data from federal government databases, called Integrated Planning and Budgeting System, known as SIOP. SIOP data refers only to grants, funds given to municipalities by the federal government to run programs within defined guidelines. Preliminary results from SIOP show decrease in Federal Grants to Brazilian Municipalities, proportional to the city size. For the initial budget allocation, &#946 was found to be roughly 0.63 for municipalities above twenty thousand inhabitants; to be roughly 0.92 for municipalities above twenty thousand inhabitants; and to be roughly 1.18 for municipalities above fifty thousand inhabitants. The second data source is DOU, government journal for publishing official acts. DOU data should give us information not only about grants, but also about FGTS funds for basic sanitation loans. In order to extract data from DOU we have applied Natural Language Processing (NLP) tools. These techniques often work better when the algorithms are provided with annotations metadata that provides additional information about the text. In particular, we fed a database with annotations into a bidirectional LSTM model applied to POS Tagging and Named-entity Recognition. Preliminary results are reported in the paper

Esta pesquisa aborda o tema da Elaboração Textual para um público alvo que tem letramento nos níveis básicos e rudimentar, de acordo com a classificação do Indicador Nacional de Alfabetismo Funcional (INAF, 2009). A Elaboração Textual é definida como um conjunto de técnicas que acrescentam material redundante em textos, sendo tradicionalmente usadas a adição de definições, sinônimos, antônimos, ou qualquer informação externa com o objetivo de auxiliar na compreensão do texto. O objetivo deste projeto de mestrado foi a proposta de dois métodos originais de elaboração textual: (1) via definição das entidades mencionadas que aparecem em um texto e (2) via definições de perguntas elaboradas direcionadas aos verbos das orações de um texto. Para a primeira tarefa, usou-se um sistema de reconhecimento de entidades mencionadas da literatura, o Rembrandt, e definições curtas da enciclopédia Wikipédia, sendo este método incorporado no sistema Web FACILITA EDUCATIVO, uma das ferramentas desenvolvidas no projeto PorSimples. O método foi avaliado de forma preliminar com um pequeno grupo de leitores com baixo nível de letramento e a avaliação foi positiva, indicando que este auxílio facilitou a leitura dos usuários da avaliação. O método de geração de perguntas elaboradas aos verbos de uma oração é uma tarefa nova que foi definida, estudada, implementada e avaliada neste mestrado. A avaliação não foi realizada junto ao público alvo e sim com especialistas em processamento de língua natural que avaliaram positivamente o método e indicaram quais erros influenciam negativamente na qualidade das perguntas geradas automaticamente. Existem boas indicações de que os métodos de elaboração desenvolvidos podem ser úteis na melhoria da compreensão da leitura para o público alvo em questão, as pessoas com baixo nível de letramento / This research addresses the topic of Textual Elaboration for low-literacy readers, i.e. people at the rudimentary and basic literacy levels according to the National Indicator of Functional Literacy (INAF, 2009). Text Elaboration consists of a set of techniques that adds extra material in texts using, traditionally, definitions, synonyms, antonyms, or any external information to assist in text understanding. The main goal of this research was the proposal of two methods of Textual Elaboration: (1) the use of short definitions for Named Entities in texts and (2) assignment of wh-questions related to verbs in text. The first task used the Rembrandt named entity recognition system and short definitions of Wikipedia. It was implemented in PorSimples web Educational Facilita tool. This method was preliminarily evaluated with a small group of low-literacy readers. The evaluation results were positive, what indicates that the tool was useful for improving the text understanding. The assignment of wh-questions related to verbs task was defined, studied, implemented and assessed during this research. Its evaluation was conducted with NLP researches instead of with low-literacy readers. There are good evidences that the text elaboration methods and resources developed here are useful in helping text understanding for low-literacy readers

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-05-03T18:01:24Z No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-05-14T19:20:24Z (GMT) No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) / Made available in DSpace on 2018-05-14T19:35:09Z (GMT). No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) Previous issue date: 2017-09-14 / The treatment of textual information has been increasingly relevant in many do- mains. One of the first tasks for extracting information from texts is the Named Entities Recognition (NER), which consists of identifying references to certain entities and finding out their classification. There are many NER domains, among them the most usual are medicine and biology. One of the challenging domains in the recognition of Named Entities (NE) is the Geology domain, which is an area lacking computational linguistic resources. This thesis proposes a method for the recognition of relevant NE in the field of Geology, specifically to the subarea of Brazilian Sedimentary Basin, in Portuguese texts. Generic and geological features were defined for the generation of a machine learning model. Among the automatic approaches to NE classification, the most prominent is the Conditional Ran- dom Fields (CRF) probabilistic model. CRF has been effectively used for word processing in natural language. To generate our model, we created GeoCorpus, a reference corpus for Geological NER, annotated by specialists. Experimental evaluations were performed to compare the proposed method with other classifiers. The best results were achieved by CRF, which shows 76,78% of Precision and 54,33% of F-Measure. / O tratamento da informa??o textual torna-se cada vez mais relevante para muitos dom?nios. Nesse sentido, uma das primeira tarefas para Extra??o de Informa??es a partir de textos ? o Reconhecimento de Entidades Nomeadas (REN), que consiste na identifica??o de refer?ncias feitas a determinadas entidades e sua classifica??o. REN compreende muitos dom?nios, entre eles os mais usuais s?o medicina e biologia. Um dos dom?nios desafiadores no reconhecimento de EN ? o de Geologia, sendo essa uma ?rea carente de recursos lingu?sticos computacionais. A presente tese prop?e um m?todo para o reconhecimento de EN relevantes no dom?nio da Geologia, sub?rea Bacia Sedimentar Brasileira, em textos da l?ngua portuguesa. Definiram-se features gen?ricas e geol?gicas para a gera??o do modelo de aprendizado. Entre as abordagens autom?ticas para classifica??o de EN, a mais proeminente ? o modelo probabil?stico Conditional Random Fields (CRF). O CRF tem sido utilizado eficazmente no processamento de textos em linguagem natural. A fim de gerar um modelo de aprendizado foi criado o GeoCorpus, um corpus de refer?ncia para REN Geol?gicas, anotado por especialistas. Avalia??es experimentais foram realizadas com o objetivo de comparar o m?todo proposto com outros classificadores. Destacam-se os melhores resultados para o CRF, o qual alcan?ou 76,78% e 54,33% em Precis?o e Medida-F.

Submitted by Jadson Francisco de Jesus SILVA (jadson@uefs.br) on 2018-01-24T22:42:26Z No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5) / Made available in DSpace on 2018-01-24T22:42:26Z (GMT). No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5) Previous issue date: 2016-09-09 / The Named Entity Recognition (NER) process is the task of identifying relevant termsintextsandassigningthemalabel.Suchwordscanreferencenamesofpeople, organizations, and places. The variety of techniques that can be used in the named entityrecognitionprocessislarge.Thetechniquescanbeclassifiedintothreedistinct approaches: rule-based, machine learning and hybrid. Concerning to the machine learningapproaches,severalfactorsmayinfluenceitsaccuracy,includingtheselected classifier, the set of features extracted from the terms, the characteristics of the textual bases, and the number of entity labels. In this work, we compared classifiers that use machine learning applied to the NER task. The comparative study includes classifiers based on CRF (Conditional Random Fields), MEMM (MaximumEntropy Markov Model) and HMM (Hidden Markov Model), which are compared in two corpora in Portuguese derived from WikiNer, and HAREM, and two corporas in English derived from CoNLL-03 and WikiNer. The comparison of the classifiers shows that the CRF is superior to the other classifiers, both with Portuguese and English texts. This study also includes the comparison of the individual and joint contribution of features, including contextual features, besides the comparison ofthe NER per named entity labels, between classifiers andcorpora. / O processo de Reconhecimento de Entidades Nomeadas (REN) ? a tarefa de iden- tificar termos relevantes em textos e atribu?-los um r?tulo. Tais palavras podem referenciar nomes de pessoas, organiza??es e locais. A variedade de t?cnicas que podem ser usadas no processo de reconhecimento de entidades nomeadas ? grande. As t?cnicas podem ser classificadas em tr?s abordagens distintas: baseadas em regras, baseadas em aprendizagem de m?quina e h?bridas. No que diz respeito as abordagens de aprendizagem de m?quina, diversos fatores podem influenciar sua exatida?, incluindo o classificador selecionado, o conjunto de features extra?das dos termos, as caracter?sticas das bases textuais e o n?mero de r?tulos de entidades. Neste trabalho, comparamos classificadores que utilizam aprendizagem de m?quina aplicadas a tarefa do REN. O estudo comparativo inclui classificadores baseados no CRF (Condicional Random Fields), MEMM (Maximum Entropy Markov Model) e HMM (Hidden Markov Model), os quais s?o comparados em dois corporas em portugu?s derivados do WikiNer, e HAREM, e dois corporas em ingl?s derivados doCoNLL-03 e WikiNer. A compara??o dos classificadores demonstra que o CRF ? superior aos demais classificadores, tanto com textos em portugu?s, quanto ingl?s. Este estudo tamb?m inclui a compara??o da contribui??o, individual e em conjunto de features, incluindo features de contexto, al?m da compara??o do REN por r?otulos de entidades nomeadas, entre os classificadores e os corpora.

Esta pesquisa aborda o tema da Elaboração Textual para um público alvo que tem letramento nos níveis básicos e rudimentar, de acordo com a classificação do Indicador Nacional de Alfabetismo Funcional (INAF, 2009). A Elaboração Textual é definida como um conjunto de técnicas que acrescentam material redundante em textos, sendo tradicionalmente usadas a adição de definições, sinônimos, antônimos, ou qualquer informação externa com o objetivo de auxiliar na compreensão do texto. O objetivo deste projeto de mestrado foi a proposta de dois métodos originais de elaboração textual: (1) via definição das entidades mencionadas que aparecem em um texto e (2) via definições de perguntas elaboradas direcionadas aos verbos das orações de um texto. Para a primeira tarefa, usou-se um sistema de reconhecimento de entidades mencionadas da literatura, o Rembrandt, e definições curtas da enciclopédia Wikipédia, sendo este método incorporado no sistema Web FACILITA EDUCATIVO, uma das ferramentas desenvolvidas no projeto PorSimples. O método foi avaliado de forma preliminar com um pequeno grupo de leitores com baixo nível de letramento e a avaliação foi positiva, indicando que este auxílio facilitou a leitura dos usuários da avaliação. O método de geração de perguntas elaboradas aos verbos de uma oração é uma tarefa nova que foi definida, estudada, implementada e avaliada neste mestrado. A avaliação não foi realizada junto ao público alvo e sim com especialistas em processamento de língua natural que avaliaram positivamente o método e indicaram quais erros influenciam negativamente na qualidade das perguntas geradas automaticamente. Existem boas indicações de que os métodos de elaboração desenvolvidos podem ser úteis na melhoria da compreensão da leitura para o público alvo em questão, as pessoas com baixo nível de letramento / This research addresses the topic of Textual Elaboration for low-literacy readers, i.e. people at the rudimentary and basic literacy levels according to the National Indicator of Functional Literacy (INAF, 2009). Text Elaboration consists of a set of techniques that adds extra material in texts using, traditionally, definitions, synonyms, antonyms, or any external information to assist in text understanding. The main goal of this research was the proposal of two methods of Textual Elaboration: (1) the use of short definitions for Named Entities in texts and (2) assignment of wh-questions related to verbs in text. The first task used the Rembrandt named entity recognition system and short definitions of Wikipedia. It was implemented in PorSimples web Educational Facilita tool. This method was preliminarily evaluated with a small group of low-literacy readers. The evaluation results were positive, what indicates that the tool was useful for improving the text understanding. The assignment of wh-questions related to verbs task was defined, studied, implemented and assessed during this research. Its evaluation was conducted with NLP researches instead of with low-literacy readers. There are good evidences that the text elaboration methods and resources developed here are useful in helping text understanding for low-literacy readers

La thèse a deux objectifs : le premier est de développer un analyseur qui permet d'analyser automatiquement des sources textuelles en chinois simplifié afin de segmenter les textes en mots et de les étiqueter par catégories grammaticales, ainsi que de construire les relations syntaxiques entre les mots. Le deuxième est d'extraire des informations autour des entités et des actions qui nous intéressent à partir des textes analysés. Afin d'atteindre ces deux objectifs, nous avons traité principalement les problématiques suivantes : les ambiguïtés de segmentation, la catégorisation ; le traitement des mots inconnus dans les textes chinois ; l'ambiguïté de l'analyse syntaxique ; la reconnaissance et le typage des entités nommées. Le texte d'entrée est traité phrase par phrase. L'analyseur commence par un traitement typographique au sein des phrases afin d'identifier les écritures latines et les chiffres. Ensuite, nous segmentons la phrase en mots à l'aide de dictionnaires. Grâce aux règles linguistiques, nous créons des hypothèses de noms propres, changeons les poids des catégories ou des mots selon leur contextes gauches ou/et droits. Un modèle de langue n-gramme élaboré à partir d'un corpus d'apprentissage permet de sélectionner le meilleur résultat de segmentation et de catégorisation. Une analyse en dépendance est utilisée pour marquer les relations entre les mots. Nous effectuons une première identification d'entités nommées à la fin de l'analyse syntaxique. Ceci permet d'identifier les entités nommées en unité ou en groupe nominal et également de leur attribuer un type. Ces entités nommées sont ensuite utilisées dans l'extraction. Les règles d'extraction permettent de valider ou de changer les types des entités nommées. L'extraction des connaissances est composée des deux étapes : extraire et annoter automatiquement des contenus à partir des textes analysés ; vérifier les contenus extraits et résoudre la cohérence à travers une ontologie. / We have developed an automatic analyser and an extraction module for Chinese langage processing. The analyser performs automatic Chinese word segmentation based on linguistic rules and dictionaries, part-of-speech tagging based on n-gram statistics and dependency grammar parsing. The module allows to extract information around named entities and activities. In order to achieve these goals, we have tackled the following main issues: segmentation and part-of-speech ambiguity; unknown word identification in Chinese text; attachment ambiguity in parsing. Chinese texts are analysed sentence by sentence. Given a sentence, the analyzer begins with typographic processing to identify sequences of Latin characters and numbers. Then, dictionaries are used for preliminary segmentation into words. Linguistic-based rules are used to create proper noun hypotheses and change the weight of some word categories. These rules take into account word context. An n-gram language model is created from a training corpus and selects the best word segmentation and parts-of-speech. Dependency grammar parsing is used to annotate relations between words. A first step of named entity recognition is performed after parsing. Its goal is to identify single-word named entities and noun-phrase-based named entities and to determine their semantic type. These named entities are then used in knowledge extraction. Knowledge extraction rules are used to validate named entities or to change their types. Knowledge extraction consists of two steps: automatic content extraction and tagging from analysed text; extracted contents control and ontology-based co-reference resolution.