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Alleviating manual feature engineering for part-of-speech tagging of Twitter microposts using distributed word representations

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

Many algorithms for natural language processing rely on manual feature engineering. In this paper, we show that we can achieve state-of-the-art performance for part-of-speech tagging of Twitter microposts by solely relying on automatically inferred word embeddings as features and a neural network. By pre-training the neural network with large amounts of automatically labeled Twitter microposts to initialize the weights, we achieve a state-of-the-art accuracy of 88.9% when tagging Twitter microposts with Penn Treebank tags.
Boek: Workshop on Modern Machine Learning and Natural Language Processing, NIPS 2014Workshop on Modern Machine Learning and Natural Language Processing, Proceedings
Aantal pagina's: 1
Jaar van publicatie:2014
Toegankelijkheid:Closed