ML Anthology

Maximum Entropy Context Models for Ranking Biographical Answers to Open-Domain Definition Questions

Alejandro Figueroa, John Atkinson
AAAI 2011 pp. 1173-1179
doi:10.1609/AAAI.V25I1.8071 /aaai/2011/figueroa2011aaai-maximum/

Abstract

In the context of question-answering systems, there are several strategies for scoring candidate answers to definition queries including centroid vectors, bi-term and context language models. These techniques use only positive examples (i.e., descriptions) when building their models. In this work, a maximum entropy based extension is proposed for context language models so as to account for regularities across non-descriptions mined from web-snippets. Experiments show that this extension outperforms other strategies increasing the precision of the top five ranked answers by more than 5%. Results suggest that web-snippets are a cost-efficient source of non-descriptions, and that some relationships extracted from dependency trees are effective to mine for candidate answer sentences.

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Cite

Text

Figueroa and Atkinson. "Maximum Entropy Context Models for Ranking Biographical Answers to Open-Domain Definition Questions." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.8071

Markdown

[Figueroa and Atkinson. "Maximum Entropy Context Models for Ranking Biographical Answers to Open-Domain Definition Questions." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/figueroa2011aaai-maximum/) doi:10.1609/AAAI.V25I1.8071

BibTeX

@inproceedings{figueroa2011aaai-maximum,
  title     = {{Maximum Entropy Context Models for Ranking Biographical Answers to Open-Domain Definition Questions}},
  author    = {Figueroa, Alejandro and Atkinson, John},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {1173-1179},
  doi       = {10.1609/AAAI.V25I1.8071},
  url       = {https://mlanthology.org/aaai/2011/figueroa2011aaai-maximum/}
}