Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?

Eylon Shoshan, Kira Radinsky


Abstract
Named-entity Recognition (NER) is an important task in the NLP field , and is widely used to solve many challenges. However, in many scenarios, not all of the entities are explicitly mentioned in the text. Sometimes they could be inferred from the context or from other indicative words. Consider the following sentence: “CMA can easily hydrolyze into free acetic acid.” Although water is not mentioned explicitly, one can infer that H2O is an entity involved in the process. In this work, we present the problem of Latent Entities Extraction (LEE). We present several methods for determining whether entities are discussed in a text, even though, potentially, they are not explicitly written. Specifically, we design a neural model that handles extraction of multiple entities jointly. We show that our model, along with multi-task learning approach and a novel task grouping algorithm, reaches high performance in identifying latent entities. Our experiments are conducted on a large biological dataset from the biochemical field. The dataset contains text descriptions of biological processes, and for each process, all of the involved entities in the process are labeled, including implicitly mentioned ones. We believe LEE is a task that will significantly improve many NER and subsequent applications and improve text understanding and inference.
Anthology ID:
K18-1020
Volume:
Proceedings of the 22nd Conference on Computational Natural Language Learning
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Anna Korhonen, Ivan Titov
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
200–210
Language:
URL:
https://aclanthology.org/K18-1020
DOI:
10.18653/v1/K18-1020
Bibkey:
Cite (ACL):
Eylon Shoshan and Kira Radinsky. 2018. Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 200–210, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text? (Shoshan & Radinsky, CoNLL 2018)
Copy Citation:
PDF:
https://aclanthology.org/K18-1020.pdf
Code
 EylonSho/LatentEntitiesExtraction