Name
Abstract | ||
|---|---|---|
There's now an increase in the number of Question Answering communities where large archives of question and answer pairs are collected up over time. These archives help traditional type-specified Question Answering (QA) systems to overcome type constraints and enable a service of general types. Semantic similarity measures between sentences dominate the overall performance of such Archive-based QA systems in finding similar questions in the archive to users' requests. Available approaches to sentence similarity measurement mainly utility word-to-word similarity measures directly in a bag-of-words way. In this paper, we take the syntactic evidence into account and carry out an examination on the impact of syntactic information on the sentence similarity measurement. We also compare the performance of our syntactic information incorporated approach with some baseline retrieval models. Experiments show that our approach outperforms other models both in mean average precision (MAP) and recall. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-71701-0_84 | PAKDD |
Keywords | Field | DocType |
syntactic impact,syntactic information,archive-based qa system,available approach,utility word-to-word similarity,semantic similarity measure,question answering community,overall performance,large archives,sentence similarity measurement,syntactic evidence,semantic similarity,bag of words,mean average precision,question answering | Semantic similarity,Data mining,Question answering,Information retrieval,Similarity measure,Computer science,Sentence similarity,Natural language processing,Artificial intelligence,Recall,Syntax | Conference |
Volume | ISSN | Citations |
4426 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |