MURAX: a robust linguistic approach for question answering using an on-line encyclopedia
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Massive query expansion for relevance feedback
Massive query expansion for relevance feedback
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
The TIPSTER SUMMAC Text Summarization Evaluation
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Learning search engine specific query transformations for question answering
Proceedings of the 10th international conference on World Wide Web
Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Embedded Grammar Tags: Advancing Natural Language Interaction on the Web
IEEE Intelligent Systems
Natural Language Guided Dialogues for Accessing the Web
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Performance issues and error analysis in an open-domain question answering system
ACM Transactions on Information Systems (TOIS)
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Learning to find answers to questions on the Web
ACM Transactions on Internet Technology (TOIT)
Information extraction with term frequencies
HLT '01 Proceedings of the first international conference on Human language technology research
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Using machine learning techniques to interpret WH-questions
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
QARAB: a question answering system to support the Arabic language
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
Concept-based question answering system
Proceedings of the 2007 conference on Human interface: Part I
Probabilistic models for answer-ranking in multilingual question-answering
ACM Transactions on Information Systems (TOIS)
Highly frequent terms and sentence retrieval
SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
Semantic query expansion based on a question category concept list
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
Exploiting question concepts for query expansion
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Predicting the performance of passage retrieval for question answering
Proceedings of the 21st ACM international conference on Information and knowledge management
Information extraction as a filtering task
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hi-index | 0.00 |
We describe and evaluate an implemented system for general-knowledge question answering. The system combines techniques for standard ad-hoc information retrieval (IR), query-dependent text summarization, and shallow syntactic and semantic sentence analysis. In a series of experiments we examine the role of each statistical and linguistic knowledge source in the question-answering system. In contrast to previous results, we find first that statistical knowledge of word co-occurrences as computed by IR vector space methods can be used to quickly and accurately locate the relevant documents for each question. The use of query-dependent text summarization techniques, however, provides only small increases in performance and severely limits recall levels when inaccurate. Nevertheless, it is the text summarization component that allows subsequent linguistic filters to focus on relevant passages. We find that even very weak linguistic knowledge can offer substantial improvements over purely IRbased techniques for question answering, especially when smoothly integrated with statistical preferences computed by the IR subsystems.