Abstract
Coreference resolution (CR) aims to find all spans of a text that refer to the same entity. The F1-Scores on these task have been greatly improved by new developed End2End-approaches and transformer networks. The inclusion of CR as a pre-processing step is expected to lead to improvements in downstream tasks. The paper examines this effect with respect to word embeddings. That is, we analyze the effects of CR on six different embedding methods and evaluate them in the context of seven lexical-semantic evaluation tasks and instantiation/hypernymy detection. Especially in the last tasks we hoped for a significant increase in performance. We show that all word embedding approaches do not benefit significantly from pronoun substitution. The measurable improvements are only marginal (around 0.5% in most test cases). We explain this result with the loss of contextual information, reduction of the relative occurrence of rare words and the lack of pronouns to be replaced.- Anthology ID:
- 2020.lrec-1.4
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 27–33
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.4
- DOI:
- Cite (ACL):
- Alexander Henlein and Alexander Mehler. 2020. On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 27–33, Marseille, France. European Language Resources Association.
- Cite (Informal):
- On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks (Henlein & Mehler, LREC 2020)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2020.lrec-1.4.pdf