A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models

Mohammad Golam Sohrab, Minh Thang Pham, Makoto Miwa, Hiroya Takamura


Abstract
We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities. We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forward- and backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach. The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks. For representing span, we compare several different neural network architectures and their ensembling for the NER model. We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention. We evaluate our approach on the two sub-tasks in the shared task. Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76% on Sub-task 1 (NER) and the F-score of 79.97% (strict) on Sub-task 2 (CI).
Anthology ID:
D19-5708
Volume:
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–55
Language:
URL:
https://aclanthology.org/D19-5708
DOI:
10.18653/v1/D19-5708
Bibkey:
Cite (ACL):
Mohammad Golam Sohrab, Minh Thang Pham, Makoto Miwa, and Hiroya Takamura. 2019. A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 47–55, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models (Sohrab et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5708.pdf