Proceedings ArticleDOI
Character-based feature extraction with LSTM networks for POS-tagging task
Aibek Makazhanov,Zhandos Yessenbayev +1 more
- pp 7991654
TLDR
A LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector that can offer a solution to the out-of-vocabulary words problem.Abstract:
In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.read more
Citations
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Book ChapterDOI
Portuguese POS Tagging Using BLSTM Without Handcrafted Features
TL;DR: This paper proposes a neural network architecture for POS tagging task for both contemporary and historical Portuguese texts and applies the architecture on three Portuguese corpora, improving the tagging accuracy for Out of Vocabulary words in the Mac-Morpho corpus and in the revised Mac- Morpho.
References
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