Bridging the vocabulary gap between questions and answer sentences
作者：
Highlights：
• We introduce two novel LM-based models to relax the exact matching assumption in IR.
• The class-based model clusters words to provide a coarse-grained word representation.
• The trigger model captures pairs of trigger and target words to find word relationships.
• Different types of word co-occurrence and triggering are studied within the models.
• We further studied the combination of both models to achieve the best result.
摘要
•We introduce two novel LM-based models to relax the exact matching assumption in IR.•The class-based model clusters words to provide a coarse-grained word representation.•The trigger model captures pairs of trigger and target words to find word relationships.•Different types of word co-occurrence and triggering are studied within the models.•We further studied the combination of both models to achieve the best result.
论文关键词：Sentence retrieval,Language modeling,Word Clustering,Triggering,Question answering
论文评审过程：Received 16 July 2013, Revised 14 April 2015, Accepted 22 April 2015, Available online 12 June 2015, Version of Record 12 June 2015.
论文官网地址：https://doi.org/10.1016/j.ipm.2015.04.005
