Affiliation:
1. University of Sheffield, Sheffield, UK
Abstract
Open domain question answering has become a very active research area over the past few years, due in large measure to the stimulus of the TREC Question Answering track. This track addresses the task of finding
answers
to natural language (NL) questions (e.g.
How tall is the Eiffel Tower? Who is Aaron Copland?
) from large text collections. This task stands in contrast to the more conventional IR task of retrieving
documents
relevant to a query, where the query may be simply a collection of keywords (e.g.
Eiffel Tower, American composer, born Brooklyn NY 1900
, ...).
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Management Information Systems
Cited by
8 articles.
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1. Biased LexRank: Passage retrieval using random walks with question-based priors;Information Processing & Management;2009-01
2. French EuroWordNet Lexical Database Improvements;Computational Linguistics and Intelligent Text Processing;2007
3. Recognising Textual Entailment with Robust Logical Inference;Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment;2006
4. A Lexical Alignment Model for Probabilistic Textual Entailment;Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment;2006
5. Passage Filtering for Open-Domain Question Answering;Advances in Natural Language Processing;2006