Analysis of Identifying Linguistic Phenomena for Recognizing Inference in Text

[[abstract]]Recognizing Textual Entailment (RTE) is a task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. Although a considerable number of studies have been made on recognizing textual entailment, little is known about the power of linguistic phenomenon for recognizing inference in text. The objective of this paper is to provide a comprehensive analysis of identifying linguistic phenomena for recognizing inference in text (RITE). In this paper, we focus on RITE-VAL System Validation subtask and propose a model by using an analysis of identifying linguistic phenomena for Recognizing Inference in Text (RITE) using the development dataset of NTCIR-11 RITE-VAL subtask. The experimental results suggest that well identified linguistic phenomenon category could enhance the accuracy of textual entailment system.[[sponsorship]]IEEE[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140813~20140815[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, California, US

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

A Machine learning approach to textual entailment recognition

Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so

Cross-Lingual Textual Entailment and Applications

Textual Entailment (TE) has been proposed as a generic framework for modeling language variability. The great potential of integrating (monolingual) TE recognition components into NLP architectures has been reported in several areas, such as question answering, information retrieval, information extraction and document summarization. Mainly due to the absence of cross-lingual TE (CLTE) recognition components, similar improvements have not yet been achieved in any corresponding cross-lingual application. In this thesis, we propose and investigate Cross-Lingual Textual Entailment (CLTE) as a semantic relation between two text portions in dierent languages. We present dierent practical solutions to approach this problem by i) bringing CLTE back to the monolingual scenario, translating the two texts into the same language; and ii) integrating machine translation and TE algorithms and techniques. We argue that CLTE can be a core tech- nology for several cross-lingual NLP applications and tasks. Experiments on dierent datasets and two interesting cross-lingual NLP applications, namely content synchronization and machine translation evaluation, conrm the eectiveness of our approaches leading to successful results. As a complement to the research in the algorithmic side, we successfully explored the creation of cross-lingual textual entailment corpora by means of crowdsourcing, as a cheap and replicable data collection methodology that minimizes the manual work done by expert annotators

Benchmarking for syntax-based sentential inference

International audienceWe propose a methodology for investigat- ing how well NLP systems handle mean- ing preserving syntactic variations. We start by presenting a method for the semi automated creation of a benchmark where entailment is mediated solely by meaning preserving syntactic variations. We then use this benchmark to compare a seman- tic role labeller and two grammar based RTE systems. We argue that the proposed methodology (i) supports a modular eval- uation of the ability of NLP systems to handle the syntax/semantic interface and (ii) permits focused error mining and er- ror analysis

New resources and ideas for semantic parser induction

In this thesis, we investigate the general topic of computational natural language understanding (NLU), which has as its goal the development of algorithms and other computational methods that support reasoning about natural language by the computer. Under the classical approach, NLU models work similar to computer compilers (Aho et al., 1986), and include as a central component a semantic parser that translates natural language input (i.e., the compiler’s high-level language) to lower-level formal languages that facilitate program execution and exact reasoning. Given the difficulty of building natural language compilers by hand, recent work has centered around semantic parser induction, or on using machine learning to learn semantic parsers and semantic representations from parallel data consisting of example text-meaning pairs (Mooney, 2007a). One inherent difficulty in this data-driven approach is finding the parallel data needed to train the target semantic parsing models, given that such data does not occur naturally “in the wild” (Halevy et al., 2009). Even when data is available, the amount of domain- and language-specific data and the nature of the available annotations might be insufficient for robust machine learning and capturing the full range of NLU phenomena. Given these underlying resource issues, the semantic parsing field is in constant need of new resources and datasets, as well as novel learning techniques and task evaluations that make models more robust and adaptable to the many applications that require reliable semantic parsing. To address the main resource problem involving finding parallel data, we investigate the idea of using source code libraries, or collections of code and text documentation, as a parallel corpus for semantic parser development and introduce 45 new datasets in this domain and a new and challenging text-to-code translation task. As a way of addressing the lack of domain- and language-specific parallel data, we then use these and other benchmark datasets to investigate training se- mantic parsers on multiple datasets, which helps semantic parsers to generalize across different domains and languages and solve new tasks such as polyglot decoding and zero-shot translation (i.e., translating over and between multiple natural and formal languages and unobserved language pairs). Finally, to address the issue of insufficient annotations, we introduce a new learning framework called learning from entailment that uses entailment information (i.e., high-level inferences about whether the meaning of one sentence follows from another) as a weak learning signal to train semantic parsers to reason about the holes in their analysis and learn improved semantic representations. Taken together, this thesis contributes a wide range of new techniques and technical solutions to help build semantic parsing models with minimal amounts of training supervision and manual engineering effort, hence avoiding the resource issues described at the onset. We also introduce a diverse set of new NLU tasks for evaluating semantic parsing models, which we believe help to extend the scope and real world applicability of semantic parsing and computational NLU

A Call for Executable Linguistics Research

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200