Results 251 to 260 of about 368,749 (284)

Analysis of a Textual Entailer

2006
We present in this paper the structure of a textual entailer, offer a detailed view of lexical aspects of entailment and study the impact of syntactic information on the overall performance of the textual entailer. It is shown that lemmatization has a big impact on the lexical component of our approach and that syntax leads to accurate entailment ...
Vasile Rus, Philip M. McCarthy, Arthur C. Graesser   +2 more
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AORTE for Recognizing Textual Entailment

2009
In this paper we present the use of the AORTE system in recognizing textual entailment. AORTE allows the automatic acquisition and alignment of ontologies from text. The information resulted from aligning ontologies created from text fragments is used in classifying textual entailment.
Reda Siblini, Leila Kosseim
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Recognizing Textual Entailment

2013
In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text.
Dagan, I, Roth, D, Sommons, M, ZANZOTTO, FABIO MASSIMO   +3 more
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Paraphrase Substitution for Recognizing Textual Entailment

2007
We describe a method for recognizing textual entailment that uses the length of the longest common subsequence (LCS) between two texts as its decision criterion. Rather than requiring strict word matching in the common subsequences, we perform a flexible match using automatically generated paraphrases.
Bosma, W.E., Callison-Burch, C.
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Translators in Textual Entailment

2010
This paper presents how the size of Textual Entailment Corpus could be increased by using Translators to generate additional 〈t, h〉 pairs. Also, we show the theoretical upper bound of a Corpus expanded by translators. Then, we propose an algorithm to expand the corpus size using Translator engines starting from a RTE Corpus, and finally we show the ...
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Applying COGEX to Recognize Textual Entailment

2006
This paper describes the system that LCC has devised to perform textual entailment recognition for the PASCAL RTE Challenge. Our system transforms each text-hypothesis pair into a two-layered logic form representation that expresses the lexical, syntactic, and semantic attributes of the text and hypothesis.
Daniel Hodges, Christine Clark, Abraham Fowler, Dan I. Moldovan   +3 more
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Applying Textual Entailment to the Interpretation of Metaphor

2013 IEEE Seventh International Conference on Semantic Computing, 2013
Metaphor is a pervasive feature of human language that enables us to conceptualize and communicate abstract concepts using more concrete terminology. Unfortunately, computational models of natural language understanding - including systems for question answering, textual entailment, lexical substitution, and word-sense disambiguation - are unable to ...
Michael Mohler, Marc T. Tomlinson, David B. Bracewell   +2 more
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Recognizing Textual Entailment and Paraphrases in Portuguese

2017
The aim of textual entailment and paraphrase recognition is to determine whether the meaning of a text fragment can be inferred (is entailed) from the meaning of another text fragment. In this paper, we address the task of automatically recognizing textual entailment (RTE) and paraphrases from text written in the Portuguese language employing ...
Gil Rocha, Henrique Lopes Cardoso
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Deep Learning for Textual Entailment Recognition

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015
In this paper we propose a novel two-step procedure to recognize textual entailment. Firstly, we build a joint Restricted Boltzmann Machines (RBM) layer to learn the joint representation of the text-hypothesis pairs. Then the reconstruction error is calculated by comparing the original representation with reconstructed representation derived from the ...
Chen Lyu 0004, Yanan Lu, Donghong Ji, Bo Chen 0024   +3 more
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TE4AV: Textual Entailment for Answer Validation

2008 International Conference on Natural Language Processing and Knowledge Engineering, 2008
The textual entailment (TE) task consists of discovering unidirectional semantic inferences between the meanings of two text snippets. Taking advantage of this, in this paper we propose using the TE system as an answer validation (AV) engine to improve the performance of question answering (QA) systems and help humans in the assessment of QA systems ...
Óscar Ferrández, Rafael Muñoz 0001, Manuel Palomar   +2 more
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