Wolf, Elisabeth ; Vembu, Shankar ; Miller, Tristan
Hrsg.: Salakoski, Tapio ; Ginter, Filip ; Pyysalo, Sampo ; Pahikkala, Tapio (2006)
On the use of topic models for word completion.
doi: 10.1007/11816508_50
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2006 |
| Herausgeber: | Salakoski, Tapio ; Ginter, Filip ; Pyysalo, Sampo ; Pahikkala, Tapio |
| Autor(en): | Wolf, Elisabeth ; Vembu, Shankar ; Miller, Tristan |
| Art des Eintrags: | Bibliographie |
| Titel: | On the use of topic models for word completion |
| Sprache: | Englisch |
| Publikationsjahr: | 2006 |
| Verlag: | Springer-Verlag |
| Buchtitel: | Proceedings of the 5th International Conference on Natural Language Processing (FinTAL 2006) |
| Reihe: | Lecture Notes in Artificial Intelligence |
| Band einer Reihe: | 4139 |
| DOI: | 10.1007/11816508_50 |
| URL / URN: | https://dx.doi.org/10.1007/11816508_50 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| ID-Nummer: | TUD-CS-2006-0039 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 27 Sep 2018 21:08 |
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