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En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos que investigan la tarea de REN para un idioma especifico o desde una perspectiva independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy pocos trabajos que estudien dicha tarea para el arabe. El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una discusion sobre los resultados que benefician a la comunidad de investigadores del REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos: 1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha tarea; 2. Analizado el estado del arte del REN; 3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes tecnicas de aprendizaje automatico; 4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores, donde cada clasificador trata con una sola clase de entidades nombradas y emplea el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas adecuados para la clase de entidades nombradas en cuestion. Nuestros experimentos han sido evaluados sobre nueve conjuntos de test. / Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318

[pt] As técnicas de processamento de linguagem natural (NLP) estão se tornando populares recentemente. A gama de aplicativos que se beneficiam de NLP é extensa, desde criar sistemas de tradução automática até ajudar no marketing de um produto. Dentro de NLP, o campo de Extração de Informações (IE) é difundido; concentra-se no processamento de textos para recuperar informações específicas sobre uma determinada entidade ou conceito. Ainda assim, a comunidade de pesquisa se concentra principalmente na construção de modelos para dados na língua inglesa. Esta tese aborda três tarefas no domínio do IE: Reconhecimento de Entidade Nomeada, Extração de Relações Semânticas e Extração Conjunta de Entidade e Relação. Primeiro, criamos um novo conjunto de dados em português no domínio biomédico, descrevemos o processo de anotação e medimos suas propriedades. Além disso, desenvolvemos um novo modelo para a tarefa de Extração Conjunta de Entidade e Relação, verificando que o mesmo é competitivo em comparação com outros modelos. Finalmente, avaliamos cuidadosamente os modelos propostos em textos de idiomas diferentes do inglês e confirmamos a dominância de modelos baseados em redes neurais. / [en] Natural language processing (NLP) techniques are becoming popular recently. The range of applications that benefit from NLP is extensive, from building machine translation systems to helping market a product. Within NLP, the Information Extraction (IE) field is widespread; it focuses on processing texts to retrieve specific information about a particular entity or concept. Still, the research community mainly focuses on building models for English data. This thesis addresses three tasks in the IE domain: Named Entity Recognition, Relation Extraction, and Joint Entity and Relation Extraction. First, we created a novel Portuguese dataset in the biomedical domain, described the annotation process, and measured its properties. Also, we developed a novel model for the Joint Entity and Relation Extraction task, verifying that it is competitive compared to other models. Finally, we carefully evaluated proposed models on non-English language datasets and confirmed the dominance of neural-based models.

Das Finden von zuverlässigen Informationen über gegebene Ereignisse aus großen und dynamischen Textsammlungen, wie dem Web, ist ein wichtiges Thema. Zum Beispiel sind Rettungsteams und Versicherungsunternehmen an prägnanten Fakten über Schäden nach Katastrophen interessiert, die heutzutage online in Web-Blogs, Zeitungsartikeln, Social Media etc. zu finden sind. Solche Fakten helfen, die erforderlichen Hilfsmaßnahmen zu bestimmen und unterstützen deren Koordination. Allerdings ist das Finden, Extrahieren und Aggregieren nützlicher Informationen ein hochkomplexes Unterfangen: Es erfordert die Ermittlung geeigneter Textquellen und deren zeitliche Einordung, die Extraktion relevanter Fakten in diesen Texten und deren Aggregation zu einer verdichteten Sicht auf die Ereignisse, trotz Inkonsistenzen, vagen Angaben und Veränderungen über die Zeit. In dieser Arbeit präsentieren und evaluieren wir Techniken und Lösungen für jedes dieser Probleme, eingebettet in ein vierstufiges Framework. Die angewandten Methoden beruhen auf Verfahren des Musterabgleichs, der Verarbeitung natürlicher Sprache und des maschinellen Lernens. Zusätzlich berichten wir über die Ergebnisse zweier Fallstudien, basierend auf dem Einsatz des gesamten Frameworks: Die Ermittlung von Daten über Erdbeben und Überschwemmungen aus Webdokumenten. Unsere Ergebnisse zeigen, dass es unter bestimmten Umständen möglich ist, automatisch zuverlässige und zeitgerechte Daten aus dem Internet zu erhalten. / Finding reliable information about given events from large and dynamic text collections, such as the web, is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found today in web blogs, online newspaper articles, social media, etc. Knowing these facts helps to determine the required scale of relief operations and supports their coordination. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources and their temporal alignment, recognizing relevant facts within these texts, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this thesis, we present and evaluate techniques and solutions for each of these problems, embedded in a four-step framework. Applied methods are pattern matching, natural language processing, and machine learning. We also report the results for two case studies applying our entire framework: gathering data on earthquakes and floods from web documents. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data from the web.

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language.

Cette thèse présente une approche d'analyse des textes non-standardisé qui consiste à modéliser une chaine de traitement permettant l’annotation automatique de textes à savoir l’annotation grammaticale en utilisant une méthode d’étiquetage morphosyntaxique et l’annotation sémantique en mettant en œuvre un système de reconnaissance des entités nommées. Dans ce contexte, nous présentons un système d'analyse du Moyen Français qui est une langue en pleine évolution dont l’orthographe, le système flexionnel et la syntaxe ne sont pas stables. Les textes en Moyen Français se singularisent principalement par l’absence d’orthographe normalisée et par la variabilité tant géographique que chronologique des lexiques médiévaux.L’objectif est de mettre en évidence un système dédié à la construction de ressources linguistiques, notamment la construction des dictionnaires électroniques, se basant sur des règles de morphologie. Ensuite, nous présenterons les instructions que nous avons établies pour construire un étiqueteur morphosyntaxique qui vise à produire automatiquement des analyses contextuelles à l’aide de grammaires de désambiguïsation. Finalement, nous retracerons le chemin qui nous a conduits à mettre en place des grammaires locales permettant de retrouver les entités nommées. De ce fait, nous avons été amenés à constituer un corpus MEDITEXT regroupant des textes en Moyen Français apparus entre le fin du XIIIème et XVème siècle. / This thesis presents a non-standardized text analysis approach which consists a chain process modeling allowing the automatic annotation of texts: grammar annotation using a morphosyntactic tagging method and semantic annotation by putting in operates a system of named-entity recognition. In this context, we present a system analysis of the Middle French which is a language in the course of evolution including: spelling, the flexional system and the syntax are not stable. The texts in Middle French are mainly distinguished by the absence of normalized orthography and the geographical and chronological variability of medieval lexicons.The main objective is to highlight a system dedicated to the construction of linguistic resources, in particular the construction of electronic dictionaries, based on rules of morphology. Then, we will present the instructions that we have carried out to construct a morphosyntactic tagging which aims at automatically producing contextual analyzes using the disambiguation grammars. Finally, we will retrace the path that led us to set up local grammars to find the named entities. Hence, we were asked to create a MEDITEXT corpus of texts in Middle French between the end of the thirteenth and fifteenth centuries.

A family of Natural Language Processing (NLP) tasks such as part-of- speech (PoS) tagging, Named Entity Recognition (NER), and Multiword Expression (MWE) identification all involve assigning labels to sequences of words in text (sequence labeling). Most modern machine learning approaches to sequence labeling utilize word embeddings, learned representations of text, in which words with similar meanings have similar representations. Quite recently, contextualized word embeddings have garnered much attention because, unlike pretrained context- insensitive embeddings such as word2vec, they are able to capture word meaning in context. In this thesis, I evaluate the performance of different embedding setups (context-sensitive, context-insensitive word, as well as task-specific word, character, lemma, and PoS) on the three abovementioned sequence labeling tasks using a deep learning model (BiLSTM) and Portuguese datasets. v

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language.

Background: Most of our biomedical knowledge is only accessible through texts. The biomedical literature grows exponentially and PubMed comprises over 18.000.000 literature abstracts. Recently much effort has been put into the creation of biomedical ontologies which capture biomedical facts. The exploitation of ontologies to explore the scientific literature is a new area of research. Motivation: When people search, they have questions in mind. Answering questions in a domain requires the knowledge of the terminology of that domain. Classical search engines do not provide background knowledge for the presentation of search results. Ontology annotated structured databases allow for data-mining. The hypothesis is that ontology annotated literature databases allow for text-mining. The central problem is to associate scientific publications with ontological concepts. This is a prerequisite for ontology-based literature search. The question then is how to answer biomedical questions using ontologies and a literature corpus. Finally the task is to automate bibliometric analyses on an corpus of scientific publications. Approach: Recent joint efforts on automatically extracting information from free text showed that the applied methods are complementary. The idea is to employ the rich terminological and relational information stored in biomedical ontologies to markup biomedical text documents. Based on established semantic links between documents and ontology concepts the goal is to answer biomedical question on a corpus of documents. The entirely annotated literature corpus allows for the first time to automatically generate bibliometric analyses for ontological concepts, authors and institutions. Results: This work includes a novel annotation framework for free texts with ontological concepts. The framework allows to generate recognition patterns rules from the terminological and relational information in an ontology. Maximum entropy models can be trained to distinguish the meaning of ambiguous concept labels. The framework was used to develop a annotation pipeline for PubMed abstracts with 27,863 Gene Ontology concepts. The evaluation of the recognition performance yielded a precision of 79.9% and a recall of 72.7% improving the previously used algorithm by 25,7% f-measure. The evaluation was done on a manually created (by the original authors) curation corpus of 689 PubMed abstracts with 18,356 curations of concepts. Methods to reason over large amounts of documents with ontologies were developed. The ability to answer questions with the online system was shown on a set of biomedical question of the TREC Genomics Track 2006 benchmark. This work includes the first ontology-based, large scale, online available, up-to-date bibliometric analysis for topics in molecular biology represented by GO concepts. The automatic bibliometric analysis is in line with existing, but often out-dated, manual analyses. Outlook: A number of promising continuations starting from this work have been spun off. A freely available online search engine has a growing user community. A spin-off company was funded by the High-Tech Gründerfonds which commercializes the new ontology-based search paradigm. Several off-springs of GoPubMed including GoWeb (general web search), Go3R (search in replacement, reduction, refinement methods for animal experiments), GoGene (search in gene/protein databases) are developed.

Donner du sens aux données textuelles est une besoin essentielle pour faire les ordinateurs comprendre notre langage. Pour extraire des informations exploitables du texte, nous devons les représenter avec des descripteurs avant d’utiliser des techniques d’apprentissage. Dans ce sens, le but de cette thèse est de faire la lumière sur les représentations hétérogènes des mots et sur la façon de les exploiter tout en abordant leur nature implicitement éparse.Dans un premier temps, nous proposons un modèle de réseau basé sur des hypergraphes qui contient des données linguistiques hétérogènes dans un seul modèle unifié. En d’autres termes, nous introduisons un modèle qui représente les mots au moyen de différentes propriétés linguistiques et les relie ensemble en fonction desdites propriétés. Notre proposition diffère des autres types de réseaux linguistiques parce que nous visons à fournir une structure générale pouvant contenir plusieurstypes de caractéristiques descriptives du texte, au lieu d’une seule comme dans la plupart des représentations existantes.Cette représentation peut être utilisée pour analyser les propriétés inhérentes du langage à partir de différents points de vue, oupour être le point de départ d’un pipeline de tâches du traitement automatique de langage. Deuxièmement, nous utilisons des techniques de fusion de caractéristiques pour fournir une représentation enrichie unique qui exploite la nature hétérogènedu modèle et atténue l’eparsité de chaque représentation. Ces types de techniques sont régulièrement utilisés exclusivement pour combiner des données multimédia.Dans notre approche, nous considérons différentes représentations de texte comme des sources d’information distinctes qui peuvent être enrichies par elles-mêmes. Cette approche n’a pas été explorée auparavant, à notre connaissance. Troisièmement, nous proposons un algorithme qui exploite les caractéristiques du réseau pour identifier et grouper des mots liés sémantiquement en exploitant les propriétés des réseaux. Contrairement aux méthodes similaires qui sont également basées sur la structure du réseau, notre algorithme réduit le nombre de paramètres requis et surtout, permet l’utilisation de réseaux lexicaux ou syntaxiques pour découvrir les groupes de mots, au lieu d’un type unique des caractéristiques comme elles sont habituellement employées.Nous nous concentrons sur deux tâches différentes de traitement du langage naturel: l’induction et la désambiguïsation des sens des mots (en anglais, Word Sense, Induction and Disambiguation, ou WSI/WSD) et la reconnaissance d’entité nommées(en anglais, Named Entity Recognition, ou NER). Au total, nous testons nos propositions sur quatre ensembles de données différents. Nous effectuons nos expériences et développements en utilisant des corpus à accès libre. Les résultats obtenus nous permettent de montrer la pertinence de nos contributions et nous donnent également un aperçu des propriétés des caractéristiques hétérogènes et de leurs combinaisons avec les méthodes de fusion. Plus précisément, nos expériences sont doubles: premièrement, nous montrons qu’en utilisant des caractéristiques hétérogènes enrichies par la fusion, provenant de notre réseau linguistique proposé, nous surpassons la performance des systèmes à caractéristiques uniques et basés sur la simple concaténation de caractéristiques. Aussi, nous analysons les opérateurs de fusion utilisés afin de mieux comprendre la raison de ces améliorations. En général, l’utilisation indépendante d’opérateurs de fusion n’est pas aussi efficace que l’utilisation d’une combinaison de ceux-ci pour obtenir une représentation spatiale finale. Et deuxièmement, nous abordons encore une fois la tâche WSI/WSD, cette fois-ci avec la méthode à base de graphes proposée afin de démontrer sa pertinence par rapport à la tâche. Nous discutons les différents résultats obtenus avec des caractéristiques lexicales ou syntaxiques. / Making sense of textual data is an essential requirement in order to make computers understand our language. To extract actionable information from text, we need to represent it by means of descriptors before using knowledge discovery techniques.The goal of this thesis is to shed light into heterogeneous representations of words and how to leverage them while addressing their implicit sparse nature.First, we propose a hypergraph network model that holds heterogeneous linguistic data in a single unified model. In other words, we introduce a model that represents words by means of different linguistic properties and links them together accordingto said properties. Our proposition differs to other types of linguistic networks in that we aim to provide a general structure that can hold several types of descriptive text features, instead of a single one as in most representations. This representationmay be used to analyze the inherent properties of language from different points of view, or to be the departing point of an applied NLP task pipeline. Secondly, we employ feature fusion techniques to provide a final single enriched representation that exploits the heterogeneous nature of the model and alleviates the sparseness of each representation.These types of techniques are regularly used exclusively to combine multimedia data. In our approach, we consider different text representations as distinct sources of information which can be enriched by themselves. This approach has not been explored before, to the best of our knowledge. Thirdly, we propose an algorithm that exploits the characteristics of the network to identify and group semantically related words by exploiting the real-world properties of the networks. In contrast with similar methods that are also based on the structure of the network, our algorithm reduces the number of required parameters and more importantly, allows for the use of either lexical or syntactic networks to discover said groups of words, instead of the singletype of features usually employed.We focus on two different natural language processing tasks: Word Sense Induction and Disambiguation (WSI/WSD), and Named Entity Recognition (NER). In total, we test our propositions on four different open-access datasets. The results obtained allow us to show the pertinence of our contributions and also give us some insights into the properties of heterogeneous features and their combinations with fusion methods. Specifically, our experiments are twofold: first, we show that using fusion-enriched heterogeneous features, coming from our proposed linguistic network, we outperform the performance of single features’ systems and other basic baselines. We note that using single fusion operators is not efficient compared to using a combination of them in order to obtain a final space representation. We show that the features added by each combined fusion operation are important towards the models predicting the appropriate classes. We test the enriched representations on both WSI/WSD and NER tasks. Secondly, we address the WSI/WSD task with our network-based proposed method. While based on previous work, we improve it by obtaining better overall performance and reducing the number of parameters needed. We also discuss the use of either lexical or syntactic networks to solve the task.Finally, we parse a corpus based on the English Wikipedia and then store it following the proposed network model. The parsed Wikipedia version serves as a linguistic resource to be used by other researchers. Contrary to other similar resources, insteadof just storing its part of speech tag and its dependency relations, we also take into account the constituency-tree information of each word analyzed. The hope is for this resource to be used on future developments without the need to compile suchresource from zero.