Textual Entailment Through Extended Lexical Overlap and Lexico-Semantic Matching
نویسندگان
چکیده
This paper presents two systems for textual entailment, both employing decision trees as a supervised learning algorithm. The first one is based primarily on the concept of lexical overlap, considering a bag of words similarity overlap measure to form a mapping of terms in the hypothesis to the source text. The second system is a lexicosemantic matching between the text and the hypothesis that attempts an alignment between chunks in the hypothesis and chunks in the text, and a representation of the text and hypothesis as two dependency graphs. Their performances are compared and their positive and negative aspects are analyzed.
منابع مشابه
Linguistic-based computational treatment of textual entailment recognition
In this thesis, I investigate how lexical resources based on the organisation of lexical knowledge in classes which share common (syntactic, semantic, etc.) features support natural language processing and in particular symbolic recognition of textual entailment. First, I present a robust and wide coverage approach to lexico-structural verb paraphrase recognition based on Levin’s (1993) classif...
متن کاملApproaching Textual Entailment with LFG and FrameNet Frames
We present a baseline system for modeling textual entailment that combines deep syntactic analysis with structured lexical meaning descriptions in the FrameNet paradigm. Textual entailment is approximated by degrees of structural and semantic overlap of text and hypothesis, which we measure in a match graph. The encoded measures of similarity are processed in a machine learning setting.1
متن کاملRecognizing Textual Entailment with LCC’s GROUNDHOG System
We introduce a new system for recognizing textual entailment (known as GROUNDHOG) which utilizes a classification-based approach to combine lexico-semantic information derived from text processing applications with a large collection of paraphrases acquired automatically from the WWW. Trained on 200,000 examples of textual entailment extracted from newswire corpora, our system managed to classi...
متن کاملRecognizing Textual Entailment Is lexical similarity enough?
We describe the system we used at the PASCAL-2005 Recognizing Textual Entailment Challenge. Our method for recognizing entailment is based on calculating “directed” sentence similarity: checking the directed “semantic” word overlap between the text and the hypothesis. We use frequency-based term weighting in combination with two different lexical similarity measures. Although one version of the...
متن کاملFBK: Cross-Lingual Textual Entailment Without Translation
This paper overviews FBK’s participation in the Cross-Lingual Textual Entailment for Content Synchronization task organized within SemEval-2012. Our participation is characterized by using cross-lingual matching features extracted from lexical and semantic phrase tables and dependency relations. The features are used for multi-class and binary classification using SVMs. Using a combination of l...
متن کامل