Momtazi, Saeedeh ; Gurevych, Iryna (2018)
Unsupervised Latent Dirichlet Allocation for supervised question classification.
In: Information Processing & Management, 54 (3)
doi: 10.1016/j.ipm.2018.11.007
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Question answering systems assist users in satisfying their information needs more precisely by providing focused responses to their questions. Among the various systems developed for such a purpose, community-based question answering has recently received researchers’ attention due to the large amount of user-generated questions and answers in social question-and-answer platforms. Reusing such data sources requires an accurate information retrieval component enhanced by a question classifier. The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question. In this paper, we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering. Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data. The learned topics are then used in the training phase to find their association with the available category labels in the training data. The category mixture of topics is finally used to predict the label of unseen data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Momtazi, Saeedeh ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Unsupervised Latent Dirichlet Allocation for supervised question classification |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Mai 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Information Processing & Management |
| Jahrgang/Volume einer Zeitschrift: | 54 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.1016/j.ipm.2018.11.007 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S030645731... |
| Kurzbeschreibung (Abstract): | Question answering systems assist users in satisfying their information needs more precisely by providing focused responses to their questions. Among the various systems developed for such a purpose, community-based question answering has recently received researchers’ attention due to the large amount of user-generated questions and answers in social question-and-answer platforms. Reusing such data sources requires an accurate information retrieval component enhanced by a question classifier. The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question. In this paper, we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering. Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data. The learned topics are then used in the training phase to find their association with the available category labels in the training data. The category mixture of topics is finally used to predict the label of unseen data. |
| Freie Schlagworte: | UKP_p_QAEduInf |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 18 Dez 2018 10:55 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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