Identifying lexical relationships and entailments with distributional semantics

Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend heavily on lexical resources to provide critical information about relationships between words, but these lexical resources are expensive to create and maintain, and are never fully comprehensive. Distributional Semantics has long offered methods for automatically inducing meaning representations from large corpora, with little or no annotation efforts. The resulting representations are valuable proxies of semantic similarity, but simply knowing two words are similar cannot tell us their relationship, or whether one entails the other. In this thesis, we consider how methods from Distributional Semantics may be applied to the difficult task of lexical entailment, where one must predict whether one word implies another. We approach this by showing contributions in areas of hypernymy detection, lexical relationship prediction, lexical substitution, and textual entailment. We propose novel experimental setups, models, analysis, and interpretations, which ultimate provide us with a better understanding of both the nature of lexical entailment, as well as the information available within distributional representations.Computer Science

Investigations into the value of labeled and unlabeled data in biomedical entity recognition and word sense disambiguation

Human annotations, especially in highly technical domains, are expensive and time consuming togather, and can also be erroneous. As a result, we never have sufficiently accurate data to train andevaluate supervised methods. In this thesis, we address this problem by taking a semi-supervised approach to biomedical namedentity recognition (NER), and by proposing an inventory-independent evaluation framework for supervised and unsupervised word sense disambiguation. Our contributions are as follows: We introduce a novel graph-based semi-supervised approach to named entity recognition(NER) and exploit pre-trained contextualized word embeddings in several biomedical NER tasks. We propose a new evaluation framework for word sense disambiguation that permits a fair comparison between supervised methods trained on different sense inventories as well as unsupervised methods without a fixed sense inventory

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge

Predicate Matrix: an interoperable lexical knowledge base for predicates

183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas

The Acquisition Of Lexical Knowledge From The Web For Aspects Of Semantic Interpretation

This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to iii stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimallysupervised; they do not require training data created by humans, though they may use humancreated data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results – a 20.5% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation

Automatic text summarisation using linguistic knowledge-based semantics

Text summarisation is reducing a text document to a short substitute summary. Since the commencement of the field, almost all summarisation research works implemented to this date involve identification and extraction of the most important document/cluster segments, called extraction. This typically involves scoring each document sentence according to a composite scoring function consisting of surface level and semantic features. Enabling machines to analyse text features and understand their meaning potentially requires both text semantic analysis and equipping computers with an external semantic knowledge. This thesis addresses extractive text summarisation by proposing a number of semantic and knowledge-based approaches. The work combines the high-quality semantic information in WordNet, the crowdsourced encyclopaedic knowledge in Wikipedia, and the manually crafted categorial variation in CatVar, to improve the summary quality. Such improvements are accomplished through sentence level morphological analysis and the incorporation of Wikipedia-based named-entity semantic relatedness while using heuristic algorithms. The study also investigates how sentence-level semantic analysis based on semantic role labelling (SRL), leveraged with a background world knowledge, influences sentence textual similarity and text summarisation. The proposed sentence similarity and summarisation methods were evaluated on standard publicly available datasets such as the Microsoft Research Paraphrase Corpus (MSRPC), TREC-9 Question Variants, and the Document Understanding Conference 2002, 2005, 2006 (DUC 2002, DUC 2005, DUC 2006) Corpora. The project also uses Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for the quantitative assessment of the proposed summarisers’ performances. Results of our systems showed their effectiveness as compared to related state-of-the-art summarisation methods and baselines. Of the proposed summarisers, the SRL Wikipedia-based system demonstrated the best performance

Towards the extraction of cross-sentence relations through event extraction and entity coreference

Cross-sentence relation extraction deals with the extraction of relations beyond the sentence boundary. This thesis focuses on two of the NLP tasks which are of importance to the successful extraction of cross-sentence relation mentions: event extraction and coreference resolution. The first part of the thesis focuses on addressing data sparsity issues in event extraction. We propose a self-training approach for obtaining additional labeled examples for the task. The process starts off with a Bi-LSTM event tagger trained on a small labeled data set which is used to discover new event instances in a large collection of unstructured text. The high confidence model predictions are selected to construct a data set of automatically-labeled training examples. We present several ways in which the resulting data set can be used for re-training the event tagger in conjunction with the initial labeled data. The best configuration achieves statistically significant improvement over the baseline on the ACE 2005 test set (macro-F1), as well as in a 10-fold cross validation (micro- and macro-F1) evaluation. Our error analysis reveals that the augmentation approach is especially beneficial for the classification of the most under-represented event types in the original data set. The second part of the thesis focuses on the problem of coreference resolution. While a certain level of precision can be reached by modeling surface information about entity mentions, their successful resolution often depends on semantic or world knowledge. This thesis investigates an unsupervised source of such knowledge, namely distributed word representations. We present several ways in which word embeddings can be utilized to extract features for a supervised coreference resolver. Our evaluation results and error analysis show that each of these features helps improve over the baseline coreference system’s performance, with a statistically significant improvement (CoNLL F1) achieved when the proposed features are used jointly. Moreover, all features lead to a reduction in the amount of precision errors in resolving references between common nouns, demonstrating that they successfully incorporate semantic information into the process

Supervised and unsupervised methods for learning representations of linguistic units

Word representations, also called word embeddings, are generic representations, often high-dimensional vectors. They map the discrete space of words into a continuous vector space, which allows us to handle rare or even unseen events, e.g. by considering the nearest neighbors. Many Natural Language Processing tasks can be improved by word representations if we extend the task specific training data by the general knowledge incorporated in the word representations. The first publication investigates a supervised, graph-based method to create word representations. This method leads to a graph-theoretic similarity measure, CoSimRank, with equivalent formalizations that show CoSimRank’s close relationship to Personalized Page-Rank and SimRank. The new formalization is efficient because it can use the graph-based word representation to compute a single node similarity without having to compute the similarities of the entire graph. We also show how we can take advantage of fast matrix multiplication algorithms. In the second publication, we use existing unsupervised methods for word representation learning and combine these with semantic resources by learning representations for non-word objects like synsets and entities. We also investigate improved word representations which incorporate the semantic information from the resource. The method is flexible in that it can take any word representations as input and does not need an additional training corpus. A sparse tensor formalization guarantees efficiency and parallelizability. In the third publication, we introduce a method that learns an orthogonal transformation of the word representation space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We use ultradense representations for a Lexicon Creation task in which words are annotated with three types of lexical information – sentiment, concreteness and frequency. The final publication introduces a new calculus for the interpretable ultradense subspaces, including polarity, concreteness, frequency and part-of-speech (POS). The calculus supports operations like “−1 × hate = love” and “give me a neutral word for greasy” (i.e., oleaginous) and extends existing analogy computations like “king − man + woman = queen”.Wortrepräsentationen, sogenannte Word Embeddings, sind generische Repräsentationen, meist hochdimensionale Vektoren. Sie bilden den diskreten Raum der Wörter in einen stetigen Vektorraum ab und erlauben uns, seltene oder ungesehene Ereignisse zu behandeln -- zum Beispiel durch die Betrachtung der nächsten Nachbarn. Viele Probleme der Computerlinguistik können durch Wortrepräsentationen gelöst werden, indem wir spezifische Trainingsdaten um die allgemeinen Informationen erweitern, welche in den Wortrepräsentationen enthalten sind. In der ersten Publikation untersuchen wir überwachte, graphenbasierte Methodenn um Wortrepräsentationen zu erzeugen. Diese Methoden führen zu einem graphenbasierten Ähnlichkeitsmaß, CoSimRank, für welches zwei äquivalente Formulierungen existieren, die sowohl die enge Beziehung zum personalisierten PageRank als auch zum SimRank zeigen. Die neue Formulierung kann einzelne Knotenähnlichkeiten effektiv berechnen, da graphenbasierte Wortrepräsentationen benutzt werden können. In der zweiten Publikation verwenden wir existierende Wortrepräsentationen und kombinieren diese mit semantischen Ressourcen, indem wir Repräsentationen für Objekte lernen, welche keine Wörter sind, wie zum Beispiel Synsets und Entitäten. Die Flexibilität unserer Methode zeichnet sich dadurch aus, dass wir beliebige Wortrepräsentationen als Eingabe verwenden können und keinen zusätzlichen Trainingskorpus benötigen. In der dritten Publikation stellen wir eine Methode vor, die eine Orthogonaltransformation des Vektorraums der Wortrepräsentationen lernt. Diese Transformation fokussiert relevante Informationen in einen ultra-kompakten Untervektorraum. Wir benutzen die ultra-kompakten Repräsentationen zur Erstellung von Wörterbüchern mit drei verschiedene Angaben -- Stimmung, Konkretheit und Häufigkeit. Die letzte Publikation präsentiert eine neue Rechenmethode für die interpretierbaren ultra-kompakten Untervektorräume -- Stimmung, Konkretheit, Häufigkeit und Wortart. Diese Rechenmethode beinhaltet Operationen wie ”−1 × Hass = Liebe” und ”neutrales Wort für Winkeladvokat” (d.h., Anwalt) und erweitert existierende Rechenmethoden, wie ”Onkel − Mann + Frau = Tante”