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On the use of topic models for word completion

Wolf, Elisabeth and Vembu, Shankar and Miller, Tristan
Salakoski, Tapio and Ginter, Filip and Pyysalo, Sampo and Pahikkala, Tapio (eds.) (2006):
On the use of topic models for word completion.
In: Proceedings of the 5th International Conference on Natural Language Processing (FinTAL 2006), Springer-Verlag, In: Lecture Notes in Artificial Intelligence, 4139, ISBN 978-3-540-37334-6,
DOI: 10.1007/11816508_50,
[Online-Edition: https://dx.doi.org/10.1007/11816508_50],
[Conference or Workshop Item]

Abstract

We investigate the use of topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), for word completion tasks. The advantage of using these models for such an application is twofold. On the one hand, they allow us to exploit semantic or contextual information when predicting candidate words for completion. On the other hand, these probabilistic models have been found to outperform classical latent semantic analysis (LSA) for modeling text documents. We describe a word completion algorithm that takes into account the semantic context of the word being typed. We also present evaluation metrics to compare different models being used in our study. Our experiments validate our hypothesis of using probabilistic models for semantic analysis of text documents and their application in word completion tasks.

Item Type: Conference or Workshop Item
Erschienen: 2006
Editors: Salakoski, Tapio and Ginter, Filip and Pyysalo, Sampo and Pahikkala, Tapio
Creators: Wolf, Elisabeth and Vembu, Shankar and Miller, Tristan
Title: On the use of topic models for word completion
Language: English
Abstract:

We investigate the use of topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), for word completion tasks. The advantage of using these models for such an application is twofold. On the one hand, they allow us to exploit semantic or contextual information when predicting candidate words for completion. On the other hand, these probabilistic models have been found to outperform classical latent semantic analysis (LSA) for modeling text documents. We describe a word completion algorithm that takes into account the semantic context of the word being typed. We also present evaluation metrics to compare different models being used in our study. Our experiments validate our hypothesis of using probabilistic models for semantic analysis of text documents and their application in word completion tasks.

Title of Book: Proceedings of the 5th International Conference on Natural Language Processing (FinTAL 2006)
Series Name: Lecture Notes in Artificial Intelligence
Volume: 4139
Publisher: Springer-Verlag
ISBN: 978-3-540-37334-6
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
DOI: 10.1007/11816508_50
Official URL: https://dx.doi.org/10.1007/11816508_50
Identification Number: TUD-CS-2006-0039
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