scholarly journals The role of sentence structure in recognizing textual entailment

Author(s):  
Catherine Blake
Author(s):  
Diane Massam

This book presents a detailed descriptive and theoretical examination of predicate-argument structure in Niuean, a Polynesian language within the Oceanic branch of the Austronesian family, spoken mainly on the Pacific island of Niue and in New Zealand. Niuean has VSO word order and an ergative case-marking system, both of which raise questions for a subject-predicate view of sentence structure. Working within a broadly Minimalist framework, this volume develops an analysis in which syntactic arguments are not merged locally to their thematic sources, but instead are merged high, above an inverted extended predicate which serves syntactically as the Niuean verb, later undergoing movement into the left periphery of the clause. The thematically lowest argument merges as an absolutive inner subject, with higher arguments merging as applicatives. The proposal relates Niuean word order and ergativity to its isolating morphology, by equating the absence of inflection with the absence of IP in Niuean, which impacts many aspects of its grammar. As well as developing a novel analysis of clause and argument structure, word order, ergative case, and theta role assignment, the volume argues for an expanded understanding of subjecthood. Throughout the volume, many other topics are also treated, such as noun incorporation, word formation, the parallel internal structure of predicates and arguments, null arguments, displacement typology, the role of determiners, and the structure of the left periphery.


Author(s):  
Masashi Yoshikawa ◽  
Koji Mineshima ◽  
Hiroshi Noji ◽  
Daisuke Bekki

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.


2008 ◽  
Vol 17 (04) ◽  
pp. 659-685 ◽  
Author(s):  
VASILE RUS ◽  
PHILIP M. McCARTHY ◽  
DANIELLE S. McNAMARA ◽  
ARTHUR C. GRAESSER

In this paper we study a graph-based approach to the task of Recognizing Textual Entailment between a Text and a Hypothesis. The approach takes into account the full lexico-syntactic context of both the Text and Hypothesis and is based on the concept of subsumption. It starts with mapping the Text and Hypothesis on to graph structures that have nodes representing concepts and edges representing lexico-syntactic relations among concepts. An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis-graph. The results obtained from a standard entailment test data set were promising. The impact of synonymy on entailment is quantified and discussed. An important advantage to a solution like ours is its ability to be customized to obtain high-confidence results.


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