Abstract
Discovering semantic relations within textual documents is a timely topic worthy of investigation. Natural language processing strategies are generally used for linking chunks of text in order to extract information that can be exploited by semantic search engines for performing complex queries. The scientific domain is an interesting area where these techniques can be applied. In this paper, we describe a system based on neural networks applied to the SemEval 2018 Task 7. The system relies on the use of word embeddings for composing the vectorial representation of text chunks. Such representations are used for feeding a neural network aims to learn the structure of paths connecting chunks associated with a specific relation. Preliminary results demonstrated the suitability of the proposed approach encouraging the investigation of this research direction.- Anthology ID:
- S18-1136
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 848–852
- Language:
- URL:
- https://aclanthology.org/S18-1136
- DOI:
- 10.18653/v1/S18-1136
- Cite (ACL):
- Mauro Dragoni. 2018. NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 848–852, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model (Dragoni, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S18-1136.pdf