A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

Context Aware Textual Entailment

In conversations, stories, news reporting, and other forms of natural language, understanding requires participants to make assumptions (hypothesis) based on background knowledge, a process called entailment. These assumptions may then be supported, contradicted, or refined as a conversation or story progresses and additional facts become known and context changes. It is often the case that we do not know an aspect of the story with certainty but rather believe it to be the case; i.e., what we know is associated with uncertainty or ambiguity. In this research a method has been developed to identify different contexts of the input raw text along with specific features of the contexts such as time, location, and objects. The method includes a two-phase SVM classifier along with a voting mechanism in the second phase to identify the contexts. Rule-based algorithms were utilized to extract the context elements. This research also develops a new context˗aware text representation. This representation maintains semantic aspects of sentences, as well as textual contexts and context elements. The method can offer both graph representation and First-Order-Logic representation of the text. This research also extracts a First-Order Logic (FOL) and XML representation of a text or series of texts. The method includes entailment using background knowledge from sources (VerbOcean and WordNet), with resolution of conflicts between extracted clauses, and handling the role of context in resolving uncertain truth

Sentence encoders for semantic textual similarity: A survey

The last decade has witnessed many accomplishments in the field of Natural Language Processing, especially in understanding the language semantics. Well-established machine learning models for generating word representation are available and has been proven useful. However, the existing techniques proposed for learning sentence level representations do not adequately capture the complexity of compositional semantics. Finding semantic similarity between sentences is a fundamental language understanding problem. In this project, we compare various machine learning models on their ability to capture the semantics of a sentence using Semantic Textual Similarity (STS) Task. We focus on models that exhibit state-of-the-art performance in Sem-Eval(2017) STS shared task. Also, we analyse the impact of models’ internal architectures on STS task performance. Out of all the models the we compared, Bi-LSTM RNN with max-pooling layer achieves the best performance in extracting a generic semantic representation and aids in better transfer learning when compared to hierarchical CNN.Natural Language Processinglanguage semanticsSemantic Textual Similarity (STS) TaskSem-Eval(2017) STSBi-LSTM RNNhierarchical CN

The Detection of Contradictory Claims in Biomedical Abstracts

Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem

Methods for Measuring Semantic Similarity of Texts

A thesis submitted in partial ful lment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyMeasuring semantic similarity is a task needed in many Natural Language Processing (NLP) applications. For example, in Machine Translation evaluation, semantic similarity is used to assess the quality of the machine translation output by measuring the degree of equivalence between a reference translation and the machine translation output. The problem of semantic similarity (Corley and Mihalcea, 2005) is de ned as measuring and recognising semantic relations between two texts. Semantic similarity covers di erent types of semantic relations, mainly bidirectional and directional. This thesis proposes new methods to address the limitations of existing work on both types of semantic relations. Recognising Textual Entailment (RTE) is a directional relation where a text T entails the hypothesis H (entailment pair) if the meaning of H can be inferred from the meaning of T (Dagan and Glickman, 2005; Dagan et al., 2013). Most of the RTE methods rely on machine learning algorithms. de Marne e et al. (2006) propose a multi-stage architecture where a rst stage determines an alignment between the T-H pairs to be followed by an entailment decision stage. A limitation of such approaches is that instead of recognising a non-entailment, an alignment that ts an optimisation criterion will be returned, but the alignment by itself is a poor predictor for iii non-entailment. We propose an RTE method following a multi-stage architecture, where both stages are based on semantic representations. Furthermore, instead of using simple similarity metrics to predict the entailment decision, we use a Markov Logic Network (MLN). The MLN is based on rich relational features extracted from the output of the predicate-argument alignment structures between T-H pairs. This MLN learns to reward pairs with similar predicates and similar arguments, and penalise pairs otherwise. The proposed methods show promising results. A source of errors was found to be the alignment step, which has low coverage. However, we show that when an alignment is found, the relational features improve the nal entailment decision. The task of Semantic Textual Similarity (STS) (Agirre et al., 2012) is de- ned as measuring the degree of bidirectional semantic equivalence between a pair of texts. The STS evaluation campaigns use datasets that consist of pairs of texts from NLP tasks such as Paraphrasing and Machine Translation evaluation. Methods for STS are commonly based on computing similarity metrics between the pair of sentences, where the similarity scores are used as features to train regression algorithms. Existing methods for STS achieve high performances over certain tasks, but poor results over others, particularly on unknown (surprise) tasks. Our solution to alleviate this unbalanced performances is to model STS in the context of Multi-task Learning using Gaussian Processes (MTL-GP) ( Alvarez et al., 2012) and state-of-the-art iv STS features ( Sari c et al., 2012). We show that the MTL-GP outperforms previous work on the same datasets