Guggilla, Chinnappa ; Miller, Tristan ; Gurevych, Iryna (2016)
CNN- and LSTM-based Claim Classification in Online User Comments.
Osaka, Japan
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes claims according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Guggilla, Chinnappa ; Miller, Tristan ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | CNN- and LSTM-based Claim Classification in Online User Comments |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2016 |
| Buchtitel: | Proceedings of the 26th International Conference on Computational Linguistics (COLING) |
| Veranstaltungsort: | Osaka, Japan |
| URL / URN: | http://aclweb.org/anthology/C/C16/C16-1258.pdf |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes claims according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks. |
| Freie Schlagworte: | UKP_reviewed |
| ID-Nummer: | TUD-CS-2016-1447 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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