Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Syntagmatic and paradigmatic representations of term variation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
SVM answer selection for open-domain question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Terminological variants for document selection and question/answer matching
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A probabilistic graphical model for joint answer ranking in question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Lexical validation of answers in Question Answering
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
The LIMSI Participation in the QAst Track
Advances in Multilingual and Multimodal Information Retrieval
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Boilerplate detection using shallow text features
Proceedings of the third ACM international conference on Web search and data mining
Detecting expected answer relations through textual entailment
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Answer type validation in question answering systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Overview of the CLEF 2006 multilingual question answering track
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Overview of the answer validation exercise 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Cross lingual question answering using QRISTAL for CLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
The effect of entity recognition on answer validation
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Towards entailment-based question answering: ITC-irst at CLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Methods combination and ML-based re-ranking of multiple hypothesis for question-answering systems
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
| Hi-index | 0.00 |
Question answering (QA) systems aim at finding answers to question posed in natural language using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a QA system based on an answer validation process able to handle Web specificity. A large number of candidate answers are extracted from short passages in order to be validated according to question and passages characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both passage and answer relevance at surface, lexical, syntactic and semantic levels to deal with different types of texts. We present and compare results obtained for factual questions posed on a Web and on a newspaper collection. We show that our system outperforms a baseline by up to 48% in MRR.