Abstract
We introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task.- Anthology ID:
- E17-1074
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 785–796
- Language:
- URL:
- https://aclanthology.org/E17-1074
- DOI:
- Cite (ACL):
- Hinrich Schütze. 2017. Nonsymbolic Text Representation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 785–796, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Nonsymbolic Text Representation (Schütze, EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/E17-1074.pdf