Collective Bio Entity Recognition in Scientific Documents using Hinge Loss Markov Random Fields
Identifying biological entities such as genes and proteins from scientific documents is crucial for further downstream tasks such as question answering and information retrieval. This task is challenging because the same surface text can refer either to a gene or a protein based on the context. Traditional approaches such as Huang et al. [12] consider the words present in the surrounding text to infer the context. However, they fail to consider the semantics of these words which are better represented by contextual word embeddings such as BERT [6] Deep learning based approaches, on the other hand, fail to make use of the relational structure of scientific documents. We introduce a novel probabilistic approach that jointly classifies all entity references using a class of undirected graphical models called hinge loss Markov random fields [1] Our approach can combine relational information with embeddingbased word semantics. Further, our approach can be easily extended to incorporate new sources of information. Our initial evaluation on the JNLPBA shared task corpus [4] shows that our joint classification approach outperforms both traditional machine learning approaches and semantic models based on word embeddings by up to 7.5% on F1 score. ACM Reference Format: Alexander Miller, Naum Markenzon, Varun Embar, and Lise Getoor. 2020. Collective Bio Entity Recognition in Scientific Documents using Hinge Loss Markov Random Fields. In Proceedings of International Workshop on Mining and Learning with Graphs (MLG '20) ACM, New York, NY, USA, 6 pages. https:/doi.org/10.1145/nnnnnnn.nnnnnnn