Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

FrameNet annotation for multimodal corpora: devising a methodology for the semantic representation of text-image interactions in audiovisual productions

Multimodal analyses have been growing in importance within several approaches to Cognitive Linguistics and applied fields such as Natural Language Understanding. Nonetheless fine-grained semantic representations of multimodal objects are still lacking, especially in terms of integrating areas such as Natural Language Processing and Computer Vision, which are key for the implementation of multimodality in Computational Linguistics. In this dissertation, we propose a methodology for extending FrameNet annotation to the multimodal domain, since FrameNet can provide fine-grained semantic representations, particularly with a database enriched by Qualia and other interframal and intraframal relations, as it is the case of FrameNet Brasil. To make FrameNet Brasil able to conduct multimodal analysis, we outlined the hypothesis that similarly to the way in which words in a sentence evoke frames and organize their elements in the syntactic locality accompanying them, visual elements in video shots may, also, evoke frames and organize their elements on the screen or work complementarily with the frame evocation patterns of the sentences narrated simultaneously to their appearance on screen, providing different profiling and perspective options for meaning construction. The corpus annotated for testing the hypothesis is composed of episodes of a Brazilian TV Travel Series critically acclaimed as an exemplar of good practices in audiovisual composition. The TV genre chosen also configures a novel experimental setting for research on integrated image and text comprehension, since, in this corpus, text is not a direct description of the image sequence but correlates with it indirectly in a myriad of ways. The dissertation also reports on an eye-tracker experiment conducted to validate the approach proposed to a text-oriented annotation. The experiment demonstrated that it is not possible to determine that text impacts gaze directly and was taken as a reinforcement to the approach of valorizing modes combination. Last, we present the Frame2 dataset, the product of the annotation task carried out for the corpus following both the methodology and guidelines proposed. The results achieved demonstrate that, at least for this TV genre but possibly also for others, a fine-grained semantic annotation tackling the diverse correlations that take place in a multimodal setting provides new perspective in multimodal comprehension modeling. Moreover, multimodal annotation also enriches the development of FrameNets, to the extent that correlations found between modalities can attest the modeling choices made by those building frame-based resources.Análises multimodais vêm crescendo em importância em várias abordagens da Linguística Cognitiva e em diversas áreas de aplicação, como o da Compreensão de Linguagem Natural. No entanto, há significativa carência de representações semânticas refinadas de objetos multimodais, especialmente em termos de integração de áreas como Processamento de Linguagem Natural e Visão Computacional, que são fundamentais para a implementação de multimodalidade no campo da Linguística Computacional. Nesta tese, propomos uma metodologia para estender o método de anotação da FrameNet ao domínio multimodal, uma vez que a FrameNet pode fornecer representações semânticas refinadas, particularmente com um banco de dados enriquecido por Qualia e outras relações interframe e intraframe, como é o caso do FrameNet Brasil. Para tornar a FrameNet Brasil capaz de realizar análises multimodais, delineamos a hipótese de que, assim como as palavras em uma frase evocam frames e organizam seus elementos na localidade sintática que os acompanha, os elementos visuais nos planos de vídeo também podem evocar frames e organizar seus elementos na tela ou trabalhar de forma complementar aos padrões de evocação de frames das sentenças narradas simultaneamente ao seu aparecimento na tela, proporcionando diferentes perfis e opções de perspectiva para a construção de sentido. O corpus anotado para testar a hipótese é composto por episódios de um programa televisivo de viagens brasileiro aclamado pela crítica como um exemplo de boas práticas em composição audiovisual. O gênero televisivo escolhido também configura um novo conjunto experimental para a pesquisa em imagem integrada e compreensão textual, uma vez que, neste corpus, o texto não é uma descrição direta da sequência de imagens, mas se correlaciona com ela indiretamente em uma miríade de formas diversa. A Tese também relata um experimento de rastreamento ocular realizado para validar a abordagem proposta para uma anotação orientada por texto. O experimento demonstrou que não é possível determinar que o texto impacta diretamente o direcionamento do olhar e foi tomado como um reforço para a abordagem de valorização da combinação de modos. Por fim, apresentamos o conjunto de dados Frame2, produto da tarefa de anotação realizada para o corpus seguindo a metodologia e as diretrizes propostas. Os resultados obtidos demonstram que, pelo menos para esse gênero de TV, mas possivelmente também para outros, uma anotação semântica refinada que aborde as diversas correlações que ocorrem em um ambiente multimodal oferece uma nova perspectiva na modelagem da compreensão multimodal. Além disso, a anotação multimodal também enriquece o desenvolvimento de FrameNets, na medida em que as correlações encontradas entre as modalidades podem atestar as escolhas de modelagem feitas por aqueles que criam recursos baseados em frames.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Word sense discovery and disambiguation

The work is based on the assumption that words with similar syntactic usage have similar meaning, which was proposed by Zellig S. Harris (1954,1968). We study his assumption from two aspects: Firstly, different meanings (word senses) of a word should manifest themselves in different usages (contexts), and secondly, similar usages (contexts) should lead to similar meanings (word senses). If we start with the different meanings of a word, we should be able to find distinct contexts for the meanings in text corpora. We separate the meanings by grouping and labeling contexts in an unsupervised or weakly supervised manner (Publication 1, 2 and 3). We are confronted with the question of how best to represent contexts in order to induce effective classifiers of contexts, because differences in context are the only means we have to separate word senses. If we start with words in similar contexts, we should be able to discover similarities in meaning. We can do this monolingually or multilingually. In the monolingual material, we find synonyms and other related words in an unsupervised way (Publication 4). In the multilingual material, we ?nd translations by supervised learning of transliterations (Publication 5). In both the monolingual and multilingual case, we first discover words with similar contexts, i.e., synonym or translation lists. In the monolingual case we also aim at finding structure in the lists by discovering groups of similar words, e.g., synonym sets. In this introduction to the publications of the thesis, we consider the larger background issues of how meaning arises, how it is quantized into word senses, and how it is modeled. We also consider how to define, collect and represent contexts. We discuss how to evaluate the trained context classi?ers and discovered word sense classifications, and ?nally we present the word sense discovery and disambiguation methods of the publications. This work supports Harris' hypothesis by implementing three new methods modeled on his hypothesis. The methods have practical consequences for creating thesauruses and translation dictionaries, e.g., for information retrieval and machine translation purposes. Keywords: Word senses, Context, Evaluation, Word sense disambiguation, Word sense discovery

Unrestricted Bridging Resolution

Anaphora plays a major role in discourse comprehension and accounts for the coherence of a text. In contrast to identity anaphora which indicates that a noun phrase refers back to the same entity introduced by previous descriptions in the discourse, bridging anaphora or associative anaphora links anaphors and antecedents via lexico-semantic, frame or encyclopedic relations. In recent years, various computational approaches have been developed for bridging resolution. However, most of them only consider antecedent selection, assuming that bridging anaphora recognition has been performed. Moreover, they often focus on subproblems, e.g., only part-of bridging or definite noun phrase anaphora. This thesis addresses the problem of unrestricted bridging resolution, i.e., recognizing bridging anaphora and finding links to antecedents where bridging anaphors are not limited to definite noun phrases and semantic relations between anaphors and their antecedents are not restricted to meronymic relations. In this thesis, we solve the problem using a two-stage statistical model. Given all mentions in a document, the first stage predicts bridging anaphors by exploring a cascading collective classification model. We cast bridging anaphora recognition as a subtask of learning fine-grained information status (IS). Each mention in a text gets assigned one IS class, bridging being one possible class. The model combines the binary classifiers for minority categories and a collective classifier for all categories in a cascaded way. It addresses the multi-class imbalance problem (e.g., the wide variation of bridging anaphora and their relative rarity compared to many other IS classes) within a multi-class setting while still keeping the strength of the collective classifier by investigating relational autocorrelation among several IS classes. The second stage finds the antecedents for all predicted bridging anaphors at the same time by exploring a joint inference model. The approach models two mutually supportive tasks (i.e., bridging anaphora resolution and sibling anaphors clustering) jointly, on the basis of the observation that semantically/syntactically related anaphors are likely to be sibling anaphors, and hence share the same antecedent. Both components are based on rich linguistically-motivated features and discriminatively trained on a corpus (ISNotes) where bridging is reliably annotated. Our approaches achieve substantial improvements over the reimplementations of previous systems for all three tasks, i.e., bridging anaphora recognition, bridging anaphora resolution and full bridging resolution. The work is – to our knowledge – the first bridging resolution system that handles the unrestricted phenomenon in a realistic setting. The methods in this dissertation were originally presented in Markert et al. (2012) and Hou et al. (2013a; 2013b; 2014). The thesis gives a detailed exposition, carrying out a thorough corpus analysis of bridging and conducting a detailed comparison of our models to others in the literature, and also presents several extensions of the aforementioned papers