Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Abstract
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2016
- DOI:
- 10.48550/arXiv.1603.03827
- arXiv:
- arXiv:1603.03827
- Bibcode:
- 2016arXiv160303827L
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing;
- Statistics - Machine Learning
- E-Print:
- Accepted as a conference paper at NAACL 2016