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Named entity disambiguation using HMMs
Abstract In this paper we present a novel approach In this paper we present a novel approach to disambiguate textual mentions of named entities against the Wikipedia knowledge base. The conditional dependencies between different named entities across Wikipedia are represented as a Markov network. In our approach, named entities are treated as hidden variables and textual mentions as observations. The number of states and observations is huge and naively using the Viterbi algorithm to find the hidden state sequence that emits the query observation sequence is computationally infeasible, given a state space of this size. Based on an observation that is specific to the disambiguation problem, we propose an approach that uses a tailored approximation to reduce the size of the state space, making the Viterbi algorithm feasible. Results show good improvement in disambiguation accuracy relative to the baseline approach and to some state-of-the-art approaches. Also, our approach shows how, with suitable approximations, HMMs can be used in such large-scale state space problems. in such large-scale state space problems.
Abstractsub In this paper we present a novel approach In this paper we present a novel approach to disambiguate textual mentions of named entities against the Wikipedia knowledge base. The conditional dependencies between different named entities across Wikipedia are represented as a Markov network. In our approach, named entities are treated as hidden variables and textual mentions as observations. The number of states and observations is huge and naively using the Viterbi algorithm to find the hidden state sequence that emits the query observation sequence is computationally infeasible, given a state space of this size. Based on an observation that is specific to the disambiguation problem, we propose an approach that uses a tailored approximation to reduce the size of the state space, making the Viterbi algorithm feasible. Results show good improvement in disambiguation accuracy relative to the baseline approach and to some state-of-the-art approaches. Also, our approach shows how, with suitable approximations, HMMs can be used in such large-scale state space problems. in such large-scale state space problems.
Bibtextype inproceedings  +
Doi 10.1109/WI-IAT.2013.173  +
Has author Alhelbawy A. + , Gaizauskas R. +
Has extra keyword Hidden variable + , Markov networks + , Named entities + , Named entity disambiguations + , Number of state + , Space problem + , State-of-the-art approach + , Wikipedia knowledge + , Intelligent agents + , Knowledge based systems + , Natural language processing systems +
Isbn 9781479929023  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 159–162  +
Published in Proceedings - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IATW 2013 +
Title Named entity disambiguation using HMMs +
Type conference paper  +
Volume 3  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 6 November 2014 14:59:57  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 14:59:57  +
DateThis property is a special property in this wiki. 2013  +
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