Non-lexical neural architecture for fine-grained POS Tagging
Matthieu Labeau,Kevin Löser,Alexandre Allauzen +2 more
- pp 232-237
TLDR
Experimental results show that the convolutional network can infer meaningful word representations, while for the prediction stage, a well designed and structured strategy allows the model to outperform stateof-the-art results, without any feature engineering.Abstract:
In this paper we explore a POS tagging application of neural architectures that can infer word representations from the raw character stream. It relies on two modelling stages that are jointly learnt: a convolutional network that infers a word representation directly from the character stream, followed by a prediction stage. Models are evaluated on a POS and morphological tagging task for German. Experimental results show that the convolutional network can infer meaningful word representations, while for the prediction stage, a well designed and structured strategy allows the model to outperform stateof-the-art results, without any feature engineering.read more
Citations
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End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
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