Foundations of statistical natural language processing
Foundations of statistical natural language processing
Discourse processing for context question answering based on linguistic knowledge
Knowledge-Based Systems
Follow-up question handling in the imix and ritel systems: A comparative study
Natural Language Engineering
Kernels on linguistic structures for answer extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A data driven approach to relevancy recognition for contextual question answering
IQA '06 Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006
Towards an empirically motivated typology of follow-up questions: the role of dialogue context
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Answering contextual questions based on ontologies and question templates
Frontiers of Computer Science in China
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With traditional Question Answering (QA) systems having reached nearly satisfactory performance, an emerging challenge is the development of successful Interactive Question Answering (IQA) systems. Important IQA subtasks are the identification of a dialogue-dependent typology of Follow Up Questions (FU Qs), automatic detection of the identified types, and the development of different context fusion strategies for each type. In this paper, we show how a system relying on shallow cues to similarity between utterances in a narrow dialogue context and other simple information sources, embedded in a machine learning framework, can improve FU Q answering performance by implicitly detecting different FU Q types and learning different context fusion strategies to help re-ranking their candidate answers.