Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Learning search engine specific query transformations for question answering
Proceedings of the 10th international conference on World Wide Web
Mining the web for answers to natural language questions
Proceedings of the tenth international conference on Information and knowledge management
Context and Page Analysis for Improved Web Search
IEEE Internet Computing
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Answer formulation for question-answering
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Towards a multilingual QA system based on the web data redundancy
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Automatic Generalization of a QA Answer Extraction Module Based on Semantic Roles
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Two Proposals of a QA Answer Extraction Module Based on Semantic Roles
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Combining semantic information in question answering systems
Information Processing and Management: an International Journal
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In this paper, we discuss our experience in using semantic constraints to improve the precision of a reformulation-based question-answering system. First, we present a method for acquiring semantic-based reformulations automatically. The goal is to generate patterns from sentences retrieved from the Web based on syntactic and semantic constraints. Once these constraints have been defined, we present a method to evaluate and re-rank candidate answers that satisfy these constraints using redundancy. The two approaches have been evaluated independently and in combination. The evaluation on about 500 questions from TREC-11 shows that the acquired semantic patterns increase the precision by 16% and the MRR by 26%, the re-ranking using semantic redundancy as well as the combined approach increase the precision by about 30% and the MRR by 67%. This shows that no manual work is now necessary to build question reformulations; while still increasing performance