Abstract
Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a ”global” word representation from all contextualized instances. We evaluate these ”pooled contextualized embeddings” on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.- Anthology ID:
- N19-1078
- 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
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 724–728
- Language:
- URL:
- https://aclanthology.org/N19-1078
- DOI:
- 10.18653/v1/N19-1078
- Cite (ACL):
- Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 2019. Pooled Contextualized Embeddings for Named Entity Recognition. 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 724–728, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Pooled Contextualized Embeddings for Named Entity Recognition (Akbik et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/N19-1078.pdf
- Data
- WNUT 2017