A semantic approach to boost passage retrieval effectiveness for question answering
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
An exploration of the principles underlying redundancy-based factoid question answering
ACM Transactions on Information Systems (TOIS)
Improving the performance of question answering with semantically equivalent answer patterns
Data & Knowledge Engineering
Terminology Extraction from Log Files
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Learning of graph-based question answering rules
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
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The question answering systems are considered the next generation of search engines. This paper focuses on the first step of this process, which is to search for relevant passages containing answers. Passage Retrieval, can be difficult because of the complexity of data, log files in our case. Our contribution is based on the enrichment of queries by using a learning method and a novel term weighting function. This original term weighting function, used within the enrichment process, aims to assign a weight to terms according to their relatedness to the context of answers. Experiments conducted on real data show that our protocol of primitive query enrichment make it possible to retrieve relevant passages.