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2019 EMNLP EMNLP 2019

IxaMed at PharmacoNER Challenge 2019

Abstract

AbstractThe aim of this paper is to present our approach (IxaMed) in the PharmacoNER 2019 task. The task consists of identifying chemical, drug, and gene/protein mentions from clinical case studies written in Spanish. The evaluation of the task is divided in two scenarios: one corresponding to the detection of named entities and one corresponding to the indexation of named entities that have been previously identified. In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings. We have achieved our best result (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records (50M words) with contextual string embeddings of Wikipedia and Electronic Health Records. On the other hand, for the indexation of the named entities we have used the Levenshtein distance obtaining a 85.34 F-Score as our best result.

🌉 Interdisciplinary Bridge — Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — clinical document
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Xabier Lahuerta , Iakes Goenaga , Koldo Gojenola , Aitziber Atutxa Salazar , Maite Oronoz

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks Natural Language Processing > Understanding > Named Entity Recognition Healthcare & Medicine > Clinical > Clinical NLP Natural Language Processing > Applications > Named Entity Recognition Deep Learning > Architectures > Recurrent Neural Networks Deep Learning > Learning Types > Sequence Labeling

Keywords

sequence labeling named entity recognition conditional random field word embedding bidirectional lstm clinical text biomedical text mining biomedical entity clinical document string embedding chemical entity clinical case study
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