Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Automatic extraction of definitions

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014This doctoral research work provides a set of methods and heuristics for building a definition extractor or for fine-tuning an existing one. In order to develop and test the architecture, a generic definitions extractor for the Portuguese language is built. Furthermore, the methods were tested in the construction of an extractor for two languages different from Portuguese, which are English and, less extensively, Dutch. The approach presented in this work makes the proposed extractor completely different in nature in comparison to the other works in the field. It is a matter of fact that most systems that automatically extract definitions have been constructed taking into account a specific corpus on a specific topic, and are based on the manual construction of a set of rules or patterns capable of identifyinf a definition in a text. This research focused on three types of definitions, characterized by the connector between the defined term and its description. The strategy adopted can be seen as a "divide and conquer"approach. Differently from the other works representing the state of the art, specific heuristics were developed in order to deal with different types of definitions, namely copula, verbal and punctuation definitions. We used different methodology for each type of definition, namely we propose to use rule-based methods to extract punctuation definitions, machine learning with sampling algorithms for copula definitions, and machine learning with a method to increase the number of positive examples for verbal definitions. This architecture is justified by the increasing linguistic complexity that characterizes the different types of definitions. Numerous experiments have led to the conclusion that the punctuation definitions are easily described using a set of rules. These rules can be easily adapted to the relevant context and translated into other languages. However, in order to deal with the other two definitions types, the exclusive use of rules is not enough to get good performance and it asks for more advanced methods, in particular a machine learning based approach. Unlike other similar systems, which were built having in mind a specific corpus or a specific domain, the one reported here is meant to obtain good results regardless the domain or context. All the decisions made in the construction of the definition extractor take into consideration this central objective.Este trabalho de doutoramento visa proporcionar um conjunto de métodos e heurísticas para a construção de um extractor de definição ou para melhorar o desempenho de um sistema já existente, quando usado com um corpus específico. A fim de desenvolver e testar a arquitectura, um extractor de definic˛ões genérico para a língua Portuguesa foi construído. Além disso, os métodos foram testados na construção de um extractor para um idioma diferente do Português, nomeadamente Inglês, algumas heurísticas também foram testadas com uma terceira língua, ou seja o Holandês. A abordagem apresentada neste trabalho torna o extractor proposto neste trabalho completamente diferente em comparação com os outros trabalhos na área. É um fato que a maioria dos sistemas de extracção automática de definicões foram construídos tendo em conta um corpus específico com um tema bem determinado e são baseados na construc˛ão manual de um conjunto de regras ou padrões capazes de identificar uma definição num texto dum domínio específico. Esta pesquisa centrou-se em três tipos de definições, caracterizadas pela ligacão entre o termo definido e a sua descrição. A estratégia adoptada pode ser vista como uma abordagem "dividir para conquistar". Diferentemente de outras pesquisa nesta área, foram desenvolvidas heurísticas específicas a fim de lidar com as diferentes tipologias de definições, ou seja, cópula, verbais e definicões de pontuação. No presente trabalho propõe-se utilizar uma metodologia diferente para cada tipo de definição, ou seja, propomos a utilização de métodos baseados em regras para extrair as definições de pontuação, aprendizagem automática, com algoritmos de amostragem para definições cópula e aprendizagem automática com um método para aumentar automáticamente o número de exemplos positivos para a definição verbal. Esta arquitetura é justificada pela complexidade linguística crescente que caracteriza os diferentes tipos de definições. Numerosas experiências levaram à conclusão de que as definições de pontuação são facilmente descritas utilizando um conjunto de regras. Essas regras podem ser facilmente adaptadas ao contexto relevante e traduzido para outras línguas. No entanto, a fim de lidar com os outros dois tipos de definições, o uso exclusivo de regras não é suficiente para obter um bom desempenho e é preciso usar métodos mais avançados, em particular aqueles baseados em aprendizado de máquina. Ao contrário de outros sistemas semelhantes, que foram construídos tendo em mente um corpus ou um domínio específico, o sistema aqui apresentado foi desenvolvido de maneira a obter bons resultados, independentemente do domínio ou da língua. Todas as decisões tomadas na construção do extractor de definição tiveram em consideração este objectivo central.Fundação para a Ciência e a Tecnologia (FCT, SFRH/ BD/36732/2007

KIND: Un proyecto de inducción automática de taxonomías léxicas

This paper presents a description of the Kind Project, an algorithm for automatic induction of lexical taxonomies from corpora. Taxonomy induction consists of the discovery of hypernymy relations between single or multiword noun pairs, and the integration of these pairs into larger structures. The proposed methodology is fundamentally statistical and the requirement of linguistic resources is minimal, a characteristic that facilitates the reproduction of experiments in different languages. The languages for which results have been obtained so far are Spanish, English and French. The implementation of the algorithm and an online demo are available as open source on the projects’ website.  Este artículo presenta una descripción del Proyecto Kind, un algoritmo para inducción automática de taxonomías léxicas a partir de corpus. La inducción de taxonomías consiste en el descubrimiento de relaciones de hiperonimia entre pares sustantivos, ya sea mono o poliléxicos, y en la integración de estos pares en estructuras mayores. La metodología propuesta es fundamentalmente estadística y tiene mínimo requerimiento de recursos lingüísticos, característica que facilita la reproducción de experimentos en distintas lenguas. Las lenguas con las que se ha experimentado hasta ahora son castellano, inglés y francés. La implementación del algoritmo y un demostrador en línea se encuentran disponibles como código abierto en el sitio web del proyecto

Automatic Generation of Lexical Resources for Opinion Mining: Models, Algorithms and Applications

Opinion mining is a recent discipline at the crossroads of Information Retrieval and of Computational Linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the relevant entities of the language that are used to express opinions, and their opinion-related properties. For example, determining that the term beautiful casts a positive connotation to its subject. In this thesis we investigate on the automatic recognition of opinion-related properties of terms. This results into building opinion-related lexical resources, which can be used into opinion mining applications. We start from the (relatively) simple problem of determining the orientation of subjective terms. We propose an original semi-supervised term classification model that is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries. This method outperforms all known methods when tested on the recognized standard benchmarks for this task. We show how our method is capable to produce good results on more complex tasks, such as discriminating subjective terms (e.g., good) from objective ones (e.g., green), or classifying terms on a fine-grained attitude taxonomy. We then propose a relevant refinement of the task, i.e., distinguishing the opinion-related properties of distinct term senses. We present SentiWordNet, a novel high-quality, high-coverage lexical resource, where each one of the 115,424 senses contained in WordNet has been automatically evaluated on the three dimensions of positivity, negativity, and objectivity. We propose also an original and effective use of random-walk models to rank term senses by their positivity or negativity. The random-walk algorithms we present have a great application potential also outside the opinion mining area, for example in word sense disambiguation tasks. A result of this experience is the generation of an improved version of SentiWordNet. We finally evaluate and compare the various versions of SentiWordNet we present here with other opinion-related lexical resources well-known in literature, experimenting their use in an Opinion Extraction application. We show that the use of SentiWordNet produces a significant improvement with respect to the baseline system, not using any specialized lexical resource, and also with respect to the use of other opinion-related lexical resources

Knowledge Organization and Terminology: application to Cork

This PhD thesis aims to prove the relevance of texts within the conceptual strand of terminological work. Our methodology serves to demonstrate how linguists can infer knowledge information from texts and subsequently systematise it, either through semiformal or formal representations. We mainly focus on the terminological analysis of specialised corpora resorting to semi-automatic tools for text analysis to systematise lexical-semantic relationships observed in specialised discourse context and subsequent modelling of the underlying conceptual system. The ultimate goal of this methodology is to propose a typology that can help lexicographers to write definitions. Based on the double dimension of Terminology, we hypothesise that text and logic modelling do not go hand in hand since the latter does not directly relate to the former. We highlight that knowledge and language are crucial for knowledge systematisation, albeit keeping in mind that they pertain to different levels of analysis, for they are not isomorphic. To meet our goals, we resorted to specialised texts produced within the industry of cork. These texts provide us with a test bed made of knowledge-rich data which enable us to demonstrate our deductive mechanisms employing the Aristotelian formula: X=Y+DC through the linguistic and conceptual analysis of the semi-automatically extracted textual data. To explore the corpus, we resorted to text mining strategies where regular expressions play a central role. The final goal of this study is to create a terminological resource for the cork industry, where two types of resources interlink, namely the CorkCorpus and the OntoCork. TermCork is a project that stems from the organisation of knowledge in the specialised field of cork. For that purpose, a terminological knowledge database is being developed to feed an e-dictionary. This e-dictionary is designed as a multilingual and multimodal product, where several resources, namely linguistic and conceptual ones are paired. OntoCork is a micro domain-ontology where the concepts are enriched with natural language definitions and complemented with images, either annotated with metainformation or enriched with hyperlinks to additional information, such as a lexicographic resource. This type of e-dictionary embodies what we consider a useful terminological tool in the current digital information society: accounting for its main features, along with an electronic format that can be integrated into the Semantic Web due to its interoperability data format. This aspect emphasises its contribution to reduce ambiguity as much as possible and to increase effective communication between experts of the domain, future experts, and language professionals.Cette thèse vise à prouver la pertinence des textes dans le volet conceptuel du travail terminologique. Notre méthodologie sert à démontrer comment les linguistes peuvent déduire des informations de connaissance à partir de textes et les systématiser par la suite, soit à travers des représentations semi-formelles ou formelles. Nous nous concentrons principalement sur l'analyse terminologique de corpus spécialisé faisant appel à des outils semi-automatiques d'analyse de texte pour systématiser les relations lexico-sémantiques observées dans un contexte de discours spécialisé et la modélisation ultérieure du système conceptuel sous-jacent. L’objectif de cette méthodologie est de proposer une typologie qui peut aider les lexicographes à rédiger des définitions. Sur la base de la double dimension de la terminologie, nous émettons l'hypothèse que la modélisation textuelle et logique ne va pas de pair puisque cette dernière n'est pas directement liée à la première. Nous soulignons que la connaissance et le langage sont essentiels pour la systématisation des connaissances, tout en gardant à l'esprit qu'ils appartiennent à différents niveaux d'analyse, car ils ne sont pas isomorphes. Pour atteindre nos objectifs, nous avons eu recours à des textes spécialisés produits dans l'industrie du liège. Ces textes nous fournissent un banc d'essai constitué de données riches en connaissances qui nous permettent de démontrer nos mécanismes déductifs utilisant la formule aristotélicienne : X = Y + DC à travers l'analyse linguistique et conceptuelle des données textuelles extraites semi-automatiquement. Pour l'exploitation du corpus, nous avons recours à des stratégies de text mining où les expressions régulières jouent un rôle central. Le but de cette étude est de créer une ressource terminologique pour l'industrie du liège, où deux types de ressources sont liés, à savoir le CorkCorpus et l'OntoCork. TermCork est un projet qui découle de l'organisation des connaissances dans le domaine spécialisé du liège. À cette fin, une base de données de connaissances terminologiques est en cours de développement pour alimenter un dictionnaire électronique. Cet edictionnaire est conçu comme un produit multilingue et multimodal, où plusieurs ressources, à savoir linguistiques et conceptuelles, sont jumelées. OntoCork est une micro-ontologie de domaine où les concepts sont enrichis de définitions de langage naturel et complétés par des images, annotées avec des méta-informations ou enrichies d'hyperliens vers des informations supplémentaires. Ce type de dictionnaire électronique désigne ce que nous considérons comme un outil terminologique utile dans la société de l'information numérique actuelle : la prise en compte de ses principales caractéristiques, ainsi qu'un format électronique qui peut être intégré dans le Web sémantique en raison de son format de données d'interopérabilité. Cet aspect met l'accent sur sa contribution à réduire autant que possible l'ambiguïté et à accroître l'efficacité de la communication entre les experts du domaine, les futurs experts et les professionnels de la langue

Terminological Methods in Lexicography: Conceptualising, Organising, and Encoding Terms in General Language Dictionaries

Os dicionários de língua geral apresentam inconsistências de uniformização e cientificidade no tratamento do conteúdo lexicográfico especializado. Analisando a presença e o tratamento de termos em dicionários de língua geral, propomos um tratamento mais uniforme e cientificamente rigoroso desse conteúdo, considerando também a necessidade de compilar e alinhar futuros recursos lexicais em consonância com padrões interoperáveis. Partimos da premissa de que o tratamento dos itens lexicais, sejam unidades lexicais (palavras em geral) ou unidades terminológicas (termos ou palavras pertencentes a determinados domínios), deve ser diferenciado, e recorremos a métodos terminológicos para tratar os termos dicionarizados. A nossa abordagem assume que a terminologia – na sua dupla dimensão linguística e conceptual – e a lexicografia, como domínios interdisciplinares, podem ser complementares. Assim, apresentamos objetivos teóricos (aperfeiçoamento da metalinguagem e descrição lexicográfica a partir de pressupostos terminológicos) e práticos (representação consistente de dados lexicográficos), que visam facilitar a organização, descrição e modelização consistente de componentes lexicográficos, nomeadamente a hierarquização das etiquetas de domínio, que são marcadores de identificação de léxico especializados. Queremos ainda facilitar a redação de definições, as quais podem ser otimizadas e elaboradas com maior precisão científica ao seguir uma abordagem terminológica no tratamento dos termos. Analisámos os dicionários desenvolvidos por três instituições académicas distintas: a Academia das Ciências de Lisboa, a Real Academia Española e a Académie Française, que representam um valioso legado da tradição lexicográfica académica europeia. A análise inicial inclui um levantamento exaustivo e a comparação das etiquetas de domínio usadas, bem como um debate sobre as opções escolhidas e um estudo comparativo do tratamento dos termos. Elaborámos, depois, uma proposta metodológica para o tratamento de termos em dicionários de língua geral, tomando como exemplo dois domínios, GEOLOGIA e FUTEBOL, extraídos da edição de 2001 do dicionário da Academia das Ciências de Lisboa. Revimos os termos selecionados de acordo com os princípios terminológicos defendidos, dando origem a sentidos especializados revistos/novos para a primeira edição digital deste dicionário. Representamos e anotamos os dados usando as especificações da TEI Lex-0, uma extensão da TEI (Text Encoding Initiative), dedicada à codificação de dados lexicográficos. Destacamos também a importância de ter etiquetas de domínio hierárquicas em vez de uma lista simples de domínios, vantajosas para a organização dos dados, correspondência e possíveis futuros alinhamentos entre diferentes recursos lexicográficos. A investigação revelou que a) os modelos estruturais dos recursos lexicais são complexos e contêm informação de natureza diversa; b) as etiquetas de domínio nos dicionários gerais da língua são planas, desequilibradas, inconsistentes e, muitas vezes, estão desatualizadas, havendo necessidade de as hierarquizar para organizar o conhecimento especializado; c) os critérios adotados para a marcação dos termos e as fórmulas utilizadas na definição são díspares; d) o tratamento dos termos é heterogéneo e formulado de diferentes formas, pelo que o recurso a métodos terminológicos podem ajudar os lexicógrafos a redigir definições; e) a aplicação de métodos terminológicos e lexicográficos interdisciplinares, e também de padrões, é vantajosa porque permite a construção de bases de dados lexicais estruturadas, concetualmente organizadas, apuradas do ponto de vista linguístico e interoperáveis. Em suma, procuramos contribuir para a questão urgente de resolver problemas que afetam a partilha, o alinhamento e vinculação de dados lexicográficos.General language dictionaries show inconsistencies in terms of uniformity and scientificity in the treatment of specialised lexicographic content. By analysing the presence and treatment of terms in general language dictionaries, we propose a more uniform and scientifically rigorous treatment of this content, considering the necessity of compiling and aligning future lexical resources according to interoperable standards. We begin from the premise that the treatment of lexical items, whether lexical units (words in general) or terminological units (terms or words belonging to particular subject fields), must be differentiated, and resort to terminological methods to treat dictionary terms. Our approach assumes that terminology – in its dual dimension, both linguistic and conceptual – and lexicography, as interdisciplinary domains, can be complementary. Thus, we present theoretical (improvement of metalanguage and lexicographic description based on terminological assumptions) and practical (consistent representation of lexicographic data) objectives that aim to facilitate the organisation, description and consistent modelling of lexicographic components, namely the hierarchy of domain labels, as they are specialised lexicon identification markers. We also want to facilitate the drafting of definitions, which can be optimised and elaborated with greater scientific precision by following a terminological approach for the treatment of terms. We analysed the dictionaries developed by three different academic institutions: the Academia das Ciências de Lisboa, the Real Academia Española and the Académie Française, which represent a valuable legacy of the European academic lexicographic tradition. The initial analysis includes an exhaustive survey and comparison of the domain labels used, as well as a debate on the chosen options and a comparative study of the treatment of the terms. We then developed a methodological proposal for the treatment of terms in general language dictionaries, exemplified using terms from two domains, GEOLOGY and FOOTBALL, taken from the 2001 edition of the dictionary of the Academia das Ciências de Lisboa. We revised the selected terms according to the defended terminological principles, giving rise to revised/new specialised meanings for the first digital edition of this dictionary. We represent and annotate the data using the TEI Lex-0 specifications, a TEI (Text Encoding Initiative) subset for encoding lexicographic data. We also highlight the importance of having hierarchical domain labels instead of a simple list of domains, which are beneficial to the data organisation itself, correspondence and possible future alignments between different lexicographic resources. Our investigation revealed the following: a) structural models of lexical resources are complex and contain information of a different nature; b) domain labels in general language dictionaries are flat, unbalanced, inconsistent and often outdated, requiring the need to hierarchise them for organising specialised knowledge; c) the criteria adopted for marking terms and the formulae used in the definition are disparate; d) the treatment of terms is heterogeneous and formulated differently, whereby terminological methods can help lexicographers to draft definitions; e) the application of interdisciplinary terminological and lexicographic methods, and of standards, is advantageous because it allows the construction of structured, conceptually organised, linguistically accurate and interoperable lexical databases. In short, we seek to contribute to the urgent issue of solving problems that affect the sharing, alignment and linking of lexicographic data