Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-06-19T11:37:24Z No. of bitstreams: 1 EVANDRO BRASIL FONSECA_TES.pdf: 1972824 bytes, checksum: 9fca0c499753cd9d2822c59040e826bf (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-26T14:40:39Z (GMT) No. of bitstreams: 1 EVANDRO BRASIL FONSECA_TES.pdf: 1972824 bytes, checksum: 9fca0c499753cd9d2822c59040e826bf (MD5) / Made available in DSpace on 2018-06-26T14:48:46Z (GMT). No. of bitstreams: 1 EVANDRO BRASIL FONSECA_TES.pdf: 1972824 bytes, checksum: 9fca0c499753cd9d2822c59040e826bf (MD5) Previous issue date: 2018-03-19 / Coreference Resolution task is challenging for Natural Language Processing, considering the required linguistic knowledge and the sophistication of language processing techniques involved. Even though it is a demanding task, a motivating factor in the study of this phenomenon is its usefulness. Basically, several Natural Language Processing tasks may benefit from their results, such as named entities recognition, relation extraction between named entities, summarization, sentiment analysis, among others. Coreference Resolution is a process that consists on identifying certain terms and expressions that refer to the same entity. For example, in the sentence ? France is refusing. The country is one of the first in the ranking... ? we can say that [the country] is a coreference of [France]. By grouping these referential terms, we form coreference groups, more commonly known as coreference chains. This thesis proposes a process for coreference resolution between noun phrases for Portuguese, focusing on the use of semantic knowledge. Our proposed approach is based on syntactic-semantic linguistic rules. That is, we combine different levels of linguistic processing, using semantic relations as support, in order to infer referential relations between mentions. Models based on linguistic rules have been efficiently applied in other languages, such as: English, Spanish and Galician. In few words, these models are more efficient than machine learning approaches when we deal with less resourceful languages, since the lack of sample-rich corpora may produce a poor training. The proposed approach is the first model for Portuguese coreference resolution which uses semantic knowledge. Thus, we consider it as the main contribution of this thesis. / A tarefa de Resolu??o de Correfer?ncia ? um grande desafio para a ?rea de Processamento da Linguagem Natural, tendo em vista o conhecimento lingu?stico exigido e a sofistica??o das t?cnicas de processamento da l?ngua empregados. Mesmo sendo uma tarefa desafiadora, um fator motivador do estudo deste fen?meno se d? pela sua utilidade. Basicamente, v?rias tarefas de Processamento da Linguagem Natural podem se beneficiar de seus resultados, como, por exemplo, o reconhecimento de entidades nomeadas, extra??o de rela??o entre entidades nomeadas, sumariza??o, an?lise de sentimentos, entre outras. A Resolu??o de Correfer?ncia ? um processo que consiste em identificar determinados termos e express?es que remetem a uma mesma entidade. Por exemplo, na senten?a ?A Fran?a est? resistindo. O pa?s ? um dos primeiros no ranking...? podemos dizer que [o pa?s] ? uma correfer?ncia de [A Fran?a]. Realizando o agrupamento desses termos referenciais, formamos grupos de men??es correferentes, mais conhecidos como cadeias de correfer?ncia. Esta tese prop?e um processo para a resolu??o de correfer?ncia entre sintagmas nominais para a l?ngua portuguesa, tendo como foco a utiliza??o do conhecimento sem?ntico. Nossa abordagem proposta ? baseada em regras lingu?sticas sint?tico-sem?nticas. Ou seja, combinamos diferentes n?veis de processamento lingu?stico utilizando rela??es sem?nticas como apoio, de forma a inferir rela??es referenciais entre men??es. Modelos baseados em regras lingu?sticas t?m sido aplicados eficientemente em outros idiomas como o ingl?s, o espanhol e o galego. Esses modelos mostram-se mais eficientes que os baseados em aprendizado de m?quina quando lidamos com idiomas menos providos de recursos, dado que a aus?ncia de corpora ricos em amostras pode prejudicar o treino desses modelos. O modelo proposto nesta tese ? o primeiro voltado para a resolu??o de correfer?ncia em portugu?s que faz uso de conhecimento sem?ntico. Dessa forma, tomamos este fator como a principal contribui??o deste trabalho.

With the constantly growing amount of biomedical literature, methods for automatically distilling information from unstructured data, collectively known as information extraction, have become indispensable. Whilst most biomedical information extraction efforts in the last decade have focussed on the identification of gene products and interactions between them, the biomedical text mining community has recently extended their scope to capture associations between biomedical and chemical entities with the aim of supporting applications in drug discovery. This thesis is the first comprehensive study focussing on information extraction from pharmaceutical chemistry literature. In this research, we describe our work on (1) recognising names of chemical compounds and drugs, facilitated by the incorporation of domain knowledge; (2) exploring different coreference resolution paradigms in order to recognise co-referring expressions given a full-text article; and (3) defining drug-target interactions as events and distilling them from pharmaceutical chemistry literature using event extraction methods.

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Coreference from the Cross-lingual Perspective Michal Nov'ak The subject of this thesis is to study properties of coreference using cross- lingual approaches. The work is motivated by the research on coreference-related linguistic typology. Another motivation is to explore whether differences in the ways how languages express coreference can be exploited to build better models for coreference resolution. We design two cross-lingual methods: the bilingually informed coreference resolution and the coreference projection. The results of our experiments with the methods carried out on Czech-English data suggest that with respect to coreference English is more informative for Czech than vice versa. Furthermore, the bilingually informed resolution applied on parallel texts has managed to outperform the monolingual resolver on both languages. In the experiments, we employ the monolingual coreference resolver and an improved method for alignment of coreferential expressions, both of which we also designed within the thesis. 1

Natural language processing (NLP) is a vibrant area of research with many practical applications today like sentiment analyses, text labeling, questioning an- swering, machine translation and automatic text summarizing. At the moment, research is mainly focused on the English language, although many other lan- guages are trying to catch up. This work focuses on an area within NLP called information extraction, and more specifically on relation extraction, that is, to ex- tract relations between entities in a text. What this work aims at is to use machine learning techniques to build a Swedish language processing pipeline with part-of- speech tagging, dependency parsing, named entity recognition and coreference resolution to use as a base for later relation extraction from archival texts. The obvious difficulty lies in the scarcity of Swedish annotated datasets. For exam- ple, no large enough Swedish dataset for coreference resolution exists today. An important part of this work, therefore, is to create a Swedish coreference solver using distantly supervised machine learning, which means creating a Swedish dataset by applying an English coreference solver on an unannotated bilingual corpus, and then using a word-aligner to translate this machine-annotated En- glish dataset to a Swedish dataset, and then training a Swedish model on this dataset. Using Allen NLP:s end-to-end coreference resolution model, both for creating the Swedish dataset and training the Swedish model, this work achieves an F1-score of 0.5. For named entity recognition this work uses the Swedish BERT models released by the Royal Library of Sweden in February 2020 and achieves an overall F1-score of 0.95. To put all of these NLP-models within a single Lan- guage Processing Pipeline, Spacy is used as a unifying framework. / Natural Language Processing (NLP) är ett stort och aktuellt forskningsområde idag med många praktiska tillämpningar som sentimentanalys, textkategoriser- ing, maskinöversättning och automatisk textsummering. Forskningen är för när- varande mest inriktad på det engelska språket, men många andra språkområ- den försöker komma ikapp. Det här arbetet fokuserar på ett område inom NLP som kallas informationsextraktion, och mer specifikt relationsextrahering, det vill säga att extrahera relationer mellan namngivna entiteter i en text. Vad det här ar- betet försöker göra är att använda olika maskininlärningstekniker för att skapa en svensk Language Processing Pipeline bestående av part-of-speech tagging, de- pendency parsing, named entity recognition och coreference resolution. Denna pipeline är sedan tänkt att användas som en bas for senare relationsextrahering från svenskt arkivmaterial. Den uppenbara svårigheten med detta ligger i att det är ont om stora, annoterade svenska dataset. Till exempel så finns det inget till- räckligt stort svenskt dataset för coreference resolution. En stor del av detta arbete går därför ut på att skapa en svensk coreference solver genom att implementera distantly supervised machine learning, med vilket menas att använda en engelsk coreference solver på ett oannoterat engelskt-svenskt corpus, och sen använda en word-aligner för att översätta detta maskinannoterade engelska dataset till ett svenskt, och sen träna en svensk coreference solver på detta dataset. Det här arbetet använder Allen NLP:s end-to-end coreference solver, både för att skapa det svenska datasetet, och för att träna den svenska modellen, och uppnår en F1-score på 0.5. Vad gäller named entity recognition så använder det här arbetet Kungliga Bibliotekets BERT-modeller som bas, och uppnår genom detta en F1- score på 0.95. Spacy används som ett enande ramverk för att samla alla dessa NLP-komponenter inom en enda pipeline.

Wikipédia est une ressource embarquée dans de nombreuses applications du traite- ment des langues naturelles. Pourtant, aucune étude à notre connaissance n’a tenté de mesurer la qualité de résolution de coréférence dans les textes de Wikipédia, une étape préliminaire à la compréhension de textes. La première partie de ce mémoire consiste à construire un corpus de coréférence en anglais, construit uniquement à partir des articles de Wikipédia. Les mentions sont étiquetées par des informations syntaxiques et séman- tiques, avec lorsque cela est possible un lien vers les entités FreeBase équivalentes. Le but est de créer un corpus équilibré regroupant des articles de divers sujets et tailles. Notre schéma d’annotation est similaire à celui suivi dans le projet OntoNotes. Dans la deuxième partie, nous allons mesurer la qualité des systèmes de détection de coréférence à l’état de l’art sur une tâche simple consistant à mesurer les mentions du concept décrit dans une page Wikipédia (p. ex : les mentions du président Obama dans la page Wiki- pédia dédiée à cette personne). Nous tenterons d’améliorer ces performances en faisant usage le plus possible des informations disponibles dans Wikipédia (catégories, redi- rects, infoboxes, etc.) et Freebase (information du genre, du nombre, type de relations avec autres entités, etc.). / Wikipedia is a resource of choice exploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource, a prelim- inary step to understand Wikipedia texts. The first part of this master thesis is to build an English coreference corpus, where all documents are from the English version of Wikipedia. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation. Our annotation scheme follows the one of OntoNotes with a few disparities. In part two, we propose a testbed for evaluating coreference systems in a simple task of measuring the particulars of the concept described in a Wikipedia page (eg. The statements of Pres- ident Obama the Wikipedia page dedicated to that person). We show that by exploiting the Wikipedia markup (categories, redirects, infoboxes, etc.) of a document, as well as links to external knowledge bases such as Freebase (information of the type, num- ber, type of relationship with other entities, etc.), we can acquire useful information on entities that helps to classify mentions as coreferent or not.

Using references in language is a major part of communication, and understanding them is not a challenge for humans. Recent years have seen increased usage of dialog systems that interact with humans in natural language to assist them in various tasks, but even the most sophisticated systems still struggle with understanding references. In this thesis, we adapt a coreference resolution system for usage in dialog systems and try to understand what is needed for an efficient understanding of references in dialog systems. We annotate a portion of logs from a customer service system and perform an analysis of the most common coreferring expressions appearing in this type of data. This analysis shows that most coreferring expressions are nominal and pronominal, and they usually appear within two sentences of each other. We implement Stanford's Multi-Pass Sieve with some adaptations and dialog-specific changes and integrate it into a dialog system framework. The preprocessing pipeline makes use of already existing NLP-tools, while some new ones are added, such as a chunker, a head-finding algorithm and a NER-like system. To analyze both user input and output of the system, we deploy two separate coreference resolution systems that interact with each other. An evaluation is performed on the system and its separate parts in five most common evaluation metrics. The system does not achieve state-of-the art numbers, but because of its domain-specific nature that is expected. Some parts of the system do not have any effect on the performance, while the dialog-specific changes contribute to it greatly. An error analysis is concluded and reveals some problems with the implementation, but more importantly, it shows how the system could be further improved by using other types of knowledge and dialog-specific features.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23438号 / 情博第768号 / 新制||情||131(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 黒橋 禎夫, 教授 河原 達也, 教授 伊藤 孝行 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Les dossiers patients électroniques contiennent des informations importantes pour la santé publique. La majeure partie de ces informations est contenue dans des documents rédigés en langue naturelle. Bien que le texte texte soit pertinent pour décrire des concepts médicaux complexes, il est difficile d'utiliser cette source de données pour l'aide à la décision, la recherche clinique ou l'analyse statistique.Parmi toutes les informations cliniques intéressantes présentes dans ces dossiers, la chronologie médicale du patient est l'une des plus importantes. Être capable d'extraire automatiquement cette chronologie permettrait d'acquérir une meilleure connaissance de certains phénomènes cliniques tels que la progression des maladies et les effets à long-terme des médicaments. De plus, cela permettrait d'améliorer la qualité des systèmes de question--réponse et de prédiction de résultats cliniques. Par ailleurs, accéder aux chronologiesmédicales est nécessaire pour évaluer la qualité du parcours de soins en le comparant aux recommandations officielles et pour mettre en lumière les étapes de ce parcours auxquelles une attention particulière doit être portée.Dans notre thèse, nous nous concentrons sur la création de ces chronologies médicales en abordant deux questions connexes en traitement automatique des langues: l'extraction d'informations temporelles et la résolution de la coréférence dans des documents cliniques.Concernant l'extraction d'informations temporelles, nous présentons une approche générique pour l'extraction de relations temporelles basée sur des traits catégoriels. Cette approche peut être appliquée sur des documents écrits en anglais ou en français. Puis, nous décrivons une approche neuronale pour l'extraction d'informations temporelles qui inclut des traits catégoriels.La deuxième partie de notre thèse porte sur la résolution de la coréférence. Nous décrivons une approche neuronale pour la résolution de la coréférence dans les documents cliniques. Nous menons une étude empirique visant à mesurer l'effet de différents composants neuronaux, tels que les mécanismes d'attention ou les représentations au niveau des caractères, sur la performance de notre approche. / Important information for public health is contained within Electronic Health Records (EHRs). The vast majority of clinical data available in these records takes the form of narratives written in natural language. Although free text is convenient to describe complex medical concepts, it is difficult to use for medical decision support, clinical research or statistical analysis.Among all the clinical aspects that are of interest in these records, the patient timeline is one of the most important. Being able to retrieve clinical timelines would allow for a better understanding of some clinical phenomena such as disease progression and longitudinal effects of medications. It would also allow to improve medical question answering and clinical outcome prediction systems. Accessing the clinical timeline is needed to evaluate the quality of the healthcare pathway by comparing it to clinical guidelines, and to highlight the steps of the pathway where specific care should be provided.In this thesis, we focus on building such timelines by addressing two related natural language processing topics which are temporal information extraction and clinical event coreference resolution.Our main contributions include a generic feature-based approach for temporal relation extraction that can be applied to documents written in English and in French. We devise a neural based approach for temporal information extraction which includes categorical features.We present a neural entity-based approach for coreference resolution in clinical narratives. We perform an empirical study to evaluate how categorical features and neural network components such as attention mechanisms and token character-level representations influence the performance of our coreference resolution approach.

This report explores possible avenues for developing coreference resolution methods for Swedish. Coreference resolution is an important topic within natural language processing, as it is used as a preprocessing step in various information extraction tasks. The topic has been studied extensively for English, but much less so for smaller languages such as Swedish. In this report we adapt two coreference resolution algorithms that were originally used for English, for use on Swedish texts. One algorithm is entirely rule-based, while the other uses machine learning. We have also annotated a Swedish dataset to be used for training and evaluation. Both algorithms showed promising results and as none clearly outperformed the other we can conclude that both would be good candidates for further development. For the rule-based algorithm more advanced rules, especially ones that could incorporate some semantic knowledge, was identified as the most important avenue of improvement. For the machine learning algorithm more training data would likely be the most beneficial. For both algorithms improved detection of mention spans would also help, as this was identified as one of the most error-prone components. / I denna rapport undersöks möjliga metoder för koreferenslösning för svenska. Koreferenslösning är en viktig uppgift inom språkteknologi, eftersom det utgör ett första steg i många typer av informationsextraktion. Uppgiften har studerats utförligt för flera större språk, framförallt engelska, men är ännu relativt outforskad för svenska och andra mindre språk. I denna rapport har vi anpassat två algoritmer som ursprungligen utvecklades för engelska för användning på svensk text. Den ena algoritmen bygger på maskininlärning och den andra är helt regelbaserad. Vi har också annoterat delar av Talbankens korpus med koreferensrelationer, för att användas för träning och utvärdering av koreferenslösningsalgoritmer. Båda algoritmerna visade lovande resultat, och ingen var tydligt bättre än den andra. Bägge vore därför lämpliga alternativ för vidareutveckling. För ML-algoritmen vore mer träningsdata den viktigaste punkten för förbättring, medan den regelbaserade algoritmen skulle kunna förbättras med mer komplexa regler, för att inkorporera exempelvis semantisk information i besluten. Ett annat viktigt utvecklingsområde är identifieringen av de fraser som utvärderas för möjlig koreferens, eftersom detta steg introducerade många fel i bägge algoritmerna.