Abstract
We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content. Our model encodes each input text by effectively identifying and integrating the representations of its semantically-relevant parts. The proposed model generates high quality representation of textual content and improves the classification performance of current state-of-the-art deep averaging networks across several text classification tasks.- Anthology ID:
- N19-1143
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1408–1414
- Language:
- URL:
- https://aclanthology.org/N19-1143
- DOI:
- 10.18653/v1/N19-1143
- Cite (ACL):
- Hadi Amiri and Mitra Mohtarami. 2019. Vector of Locally Aggregated Embeddings for Text Representation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1408–1414, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Vector of Locally Aggregated Embeddings for Text Representation (Amiri & Mohtarami, NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/N19-1143.pdf
- Data
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