C4.5: programs for machine learning
C4.5: programs for machine learning
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A machine learning approach to pronoun resolution in spoken dialogue
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Coreference for NLP applications
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Importance of pronominal anaphora resolution in question answering systems
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Experiments with interactive question-answering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Question answering with lexical chains propagating verb arguments
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Using coreference for question answering
CorefApp '99 Proceedings of the Workshop on Coreference and its Applications
Question interpretation in QA@L²F
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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The capability of handling anaphora is becoming a key feature for Question Answering systems, as it can play a crucial role at different stages of the QA loop. At the question processing stage, on which this paper is focused, it enhances the treatment of follow-up questions, allowing for a more natural interaction with the user. As much as the QA task evolves towards a realistic dialogue-based scenario, one of the concrete problems raised by follow-up questions is tracking their actual referent. Each question may in fact refer to the topic of the session, to an answer given to an earlier question, or to a new entity it introduces in the dialogue. Focusing on the referent traceability problem, we present and experiment with a possible data-driven solution which exploits simple features of the input question and its surrounding context (the target of the session, and the previous questions) to inform the next phases of the QA process.