De Neve, Wesley

Godin, Fréderic

Van de Walle, Rik

English

Ghent University Academic Bibliography

  biblio.ugent.be   The UGent Institutional Repository is the electronic archiving and dissemination platform for allUGent research publications. Ghent University has implemented a mandate stipulating that allacademic publications of UGent researchers should be deposited and archived in this repository.Except for items where current copyright restrictions apply, these papers are available in OpenAccess.   This item is the archived peer-reviewed author-version of:   Part-of-Speech Tagging of Twitter Microposts only using Distributed Word Representations and aNeural Network   Fréderic Godin, Wesley De Neve, and Rik Van de Walle   In: CLIN 2015, book of abstracts for the 25th meeting of computational linguistics in the Netherlands,45, 2015.   http://www.clips.uantwerpen.be/~ben/sites/default/files/book_of_abstracts_final.pdf     To refer to or to cite this work, please use the citation to the published version:   Godin, F., De Neve, W., and Van de Walle, R. (2015). Part-of-Speech Tagging of Twitter Micropostsonly using Distributed Word Representations and a Neural Network. CLIN 2015, book of abstractsfor the 25th meeting of computational linguistics in the Netherlands 45. Part-of-Speech Tagging of Twitter Micropostsonly using Distributed Word Representationsand a Neural NetworkFre´deric GodinMultimedia LabGhent University - iMindsGhent, Belgiumfrederic.godin@ugent.beWesley De NeveMultimedia Lab & IVY LabGhent University & KAISTGhent, Belgium & Daejeon, South Koreawesley.deneve@ugent.beRik Van de WalleMultimedia LabGhent University - iMindsGhent, Belgiumrik.vandewalle@ugent.beAbstractMany algorithms for natural language processing rely on manual feature engi-neering. However, manually finding effective features is a labor-intensive task.Moreover, whenever these algorithms are applied on new types of content, theydo not perform that well anymore and new features need to be engineered. Forexample, current algorithms developed for Part-of-Speech (PoS) tagging of newsarticles with Penn Treebank tags perform poorly on microposts posted on socialmedia. As an example, the state-of-the-art Stanford tagger trained on news articledata reaches an accuracy of 73% when PoS tagging microposts. When the Stan-ford tagger is retrained on micropost data and new micropost-specific features areadded, an accuracy of 88.7% can be obtained.We show that we can achieve state-of-the-art performance for PoS tagging of Twit-ter microposts by solely relying on automatically inferred distributed word repre-sentations as features and a neural network. To automatically infer the distributedword representations, we make use of 400 million Twitter microposts. Next, wefeed a context window of distributed word representations around the word wewant to tag to a neural network to predict the corresponding PoS tag. To initializethe weights of the neural network, we pre-train it with large amounts of automat-ically high-confidence labeled Twitter microposts. Using a data-driven approach,we finally achieve a state-of-the-art accuracy of 88.9% when tagging Twitter mi-croposts with Penn Treebank tags.1

Part-of-speech tagging of Twitter microposts only using distributed word representations and a neural network

https://biblio.ugent.be/publication/5939109/file/5939110

Part-of-speech tagging of Twitter microposts only using distributed word representations and a neural network

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