An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Textual entailment through extended lexical overlap and lexico-semantic matching
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Recognizing textual entailment using a machine learning approach
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Integrating statistical and lexical information for recognizing textual entailments in text
Knowledge-Based Systems
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Textual Entailment Recognition is a semantic inference task that is required in many natural language processing (NLP) applications. In this paper, we present our system for the third PASCAL recognizing textual entailment (RTE-3) challenge. The system is built on a machine learning framework with the following features derived by state-of-the-art NLP techniques: lexical semantic similarity (LSS), named entities (NE), dependent content word pairs (DEP), average distance (DIST), negation (NG), task (TK), and text length (LEN). On the RTE-3 test dataset, our system achieves the accuracy of 0.64 and 0.6488 for the two official submissions, respectively. Experimental results show that LSS and NE are the most effective features. Further analyses indicate that a baseline dummy system can achieve accuracy 0.545 on the RTE-3 test dataset, which makes RTE-3 relatively easier than RTE-2 and RTE-1. In addition, we demonstrate with examples that the current Average Precision measure and its evaluation process need to be changed.