Recognizing Textual Entailment Using Description Logic And Semantic Relatedness

Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) applications such as: question answering, information extraction, summarization, and even machine translation. For this reason, research on textual entailment has attracted a significant amount of attention in recent years. A robust logical-based meaning representation of text is very hard to build, therefore the majority of textual entailment approaches rely on syntactic methods or shallow semantic alternatives. In addition, approaches that do use a logical-based meaning representation, require a large knowledge base of axioms and inference rules that are rarely available. The goal of this thesis is to design an efficient description logic based approach for recognizing textual entailment that uses semantic relatedness information as an alternative to large knowledge base of axioms and inference rules. In this thesis, we propose a description logic and semantic relatedness approach to textual entailment, where the type of semantic relatedness axioms employed in aligning the description logic representations are used as indicators of textual entailment. In our approach, the text and the hypothesis are first represented in description logic. The representations are enriched with additional semantic knowledge acquired by using the web as a corpus. The hypothesis is then merged into the text representation by learning semantic relatedness axioms on demand and a reasoner is then used to reason over the aligned representation. Finally, the types of axioms employed by the reasoner are used to learn if the text entails the hypothesis or not. To validate our approach we have implemented an RTE system named AORTE, and evaluated its performance on recognizing textual entailment using the fourth recognizing textual entailment challenge. Our approach achieved an accuracy of 68.8 on the two way task and 61.6 on the three way task which ranked the approach as 2nd when compared to the other participating runs in the same challenge. These results show that our description logical based approach can effectively be used to recognize textual entailment

Hizkuntza Anitzeko Erlazio Semantikoen Erauzketa Medikuntzaren Domeinuan

Aro digital honentan datu kopuru handiena textu gordin formatuan aurkitzen da. Datu horiekin lan egiteko Informazio Erauzketa (IE) bihurtzen da oinarri gaur egungo aplikazioetan. Hizkuntzaren prozesaketa automatikoko ataza gehientxuenetan gertatu den bezala ikasketa sakonak artearen egoera ezarri du, baita IEn ere. Jakina da teknika hauek datu kopuru handiak behar dituztela errendimendu ona lortzeko. Badira hainbat domeinu eta testuinguru, datu anotatu gutxikoak, zailtasunak dituztenak ikasketa sakoneko tekniken aurrerapenak modu eraginkorrean erabiltzeko. Anotazio berriak egitea garestia izaten da orokorrean, batez ere eredu berri hauek behar duten kopuruetara iristeko. Lan honen helburu nagusia domeinu eta testuinguru hauentzako modu merke batean ikasketa sakoneko sistemen errendimendua hobetzeko teknikak esploratzea da. Zehatzago esanda, ezagutza-transferentzia eta datuen-gehikuntza automatikoa paradigmetan ikertuko dugu helburua lortzeko. Azkenik, teknika hauek baliabide urrikoa den medikuntzako domeinuko eHealth-KD 2020 ataza-partekatuan aplikatuko eta ebalutako dira, uneko artearen egoera hobetzeko helburuarekin.In this digital age the greatest amount of data is found in raw text format. Information Extraction (IE) to work with this data becomes the basis in today's applications. As has happened in most tasks of automatic language processing, deep learning has established the state of the art in IE as well. It is well known that these techniques require a large amount of data to achieve good performance. There are a number of domains and contexts, with little annotated data, that have di culties making e ective the use of advances in deep learning techniques. Making new annotations is generally expensive, especially to reach the numbers needed for these new models. The main goal of this work is to explore techniques to improve the performance of deep learning systems in a cost-e ective way for these domains and contexts. More speci cally, we will investigate transfer-learning and automatic data augmentation paradigms to achieve the goal. Finally, these techniques will be applied and evaluated in the shared task eHealth-KD 2020 in the low-resource medical domain, with the goal of improving the state of the art

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus

Aspects of Coherence for Entity Analysis

Natural language understanding is an important topic in natural language proces- sing. Given a text, a computer program should, at the very least, be able to under- stand what the text is about, and ideally also situate it in its extra-textual context and understand what purpose it serves. What exactly it means to understand what a text is about is an open question, but it is generally accepted that, at a minimum, un- derstanding involves being able to answer questions like “Who did what to whom? Where? When? How? And Why?”. Entity analysis, the computational analysis of entities mentioned in a text, aims to support answering the questions “Who?” and “Whom?” by identifying entities mentioned in a text. If the answers to “Where?” and “When?” are specific, named locations and events, entity analysis can also pro- vide these answers. Entity analysis aims to answer these questions by performing entity linking, that is, linking mentions of entities to their corresponding entry in a knowledge base, coreference resolution, that is, identifying all mentions in a text that refer to the same entity, and entity typing, that is, assigning a label such as Person to mentions of entities. In this thesis, we study how different aspects of coherence can be exploited to improve entity analysis. Our main contribution is a method that allows exploiting knowledge-rich, specific aspects of coherence, namely geographic, temporal, and entity type coherence. Geographic coherence expresses the intuition that entities mentioned in a text tend to be geographically close. Similarly, temporal coherence captures the intuition that entities mentioned in a text tend to be close in the tem- poral dimension. Entity type coherence is based in the observation that in a text about a certain topic, such as sports, the entities mentioned in it tend to have the same or related entity types, such as sports team or athlete. We show how to integrate features modeling these aspects of coherence into entity linking systems and esta- blish their utility in extensive experiments covering different datasets and systems. Since entity linking often requires computationally expensive joint, global optimi- zation, we propose a simple, but effective rule-based approach that enjoys some of the benefits of joint, global approaches, while avoiding some of their drawbacks. To enable convenient error analysis for system developers, we introduce a tool for visual analysis of entity linking system output. Investigating another aspect of co- herence, namely the coherence between a predicate and its arguments, we devise a distributed model of selectional preferences and assess its impact on a neural core- ference resolution system. Our final contribution examines how multilingual entity typing can be improved by incorporating subword information. We train and make publicly available subword embeddings in 275 languages and show their utility in a multilingual entity typing tas