Combining Character-Level Representation for Relation Classification

Autor:	Yinge Zhao, Weiran Xu, Dongyun Liang
Rok vydání:	2017
Předmět:	Word embedding Computer science business.industry Carry (arithmetic) Representation (systemics) computer.software_genre Recurrent neural network Combining character Relation classification Morpheme Word representation Artificial intelligence business computer Natural language processing
Zdroj:	Artificial Neural Networks and Machine Learning – ICANN 2017 ISBN: 9783319686110 ICANN (2)
Popis:	Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important short-distance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on the SemEval-2010 Task8 dataset for relation classification show that our model achieves a competitive result.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1c00bc137612c01468fb7c32951f8c14 https://doi.org/10.1007/978-3-319-68612-7_45 Zobrazit plný text záznamu