文章基本信息

标题：Jointly Learning Similarity Transformations for Textual Entailment
本地全文：下载
作者：Ken-ichi Yokote ; Danushka Bollegala ; Mitsuru Ishizuka 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2013
卷号：28
期号：2
页码：220-229
DOI：10.1527/tjsai.28.220
出版社：The Japanese Society for Artificial Intelligence
摘要：Predicting entailment between two given texts is an important task on which the performance of numerous NLP tasks such as question answering, text summarization, and information extraction depend.The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F -score of 46.48 on the RTE-7 benchmark dataset. The proposed method is ranked 2 -nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.
关键词：recognizing textual entailment ; semantic computing ; natural language processing