Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

By Buzhou Tang, Hongxin Cao, Xiaolong Wang, Qingcai Chen, Hua Xu. 2014

133 citations recorded by the source corpus.

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in F-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, background): “Recently, in the biomedical domain, word embeddings have been remarkably utilized in applications such as biomedical named entity recognition (NER) [14], [15], medical synonym extraction [16], relation extraction (RE) (e.g., chemical-disease relation [17], drug-drug interaction [18], [19] and protei”
Boosting Named Entity Recognition with Neural Character Embeddings (2015, background): “On the other hand, some recent work on NER have used deep learning strategies which minimize the need of these costly features (Chen et al., 2010; Collobert et al., 2011; Passos et al., 2014; Tang et al., 2014).”
Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries (2015, methodology): “Following previous studies [15,56], the dimension of word embeddings is set to 50.”
Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients (2017, background): “recognition [45,46], adverse drug event detection [47,48], ranking biomedical synonyms [49], and disambiguating clinical abbreviations [50,51].”
Word2Vec inversion and traditional text classifiers for phenotyping lupus (2017, methodology): “Often, Word2Vec is used to create word clusters by averaging word vectors into clusters, referred to as word averaging, in order to be used in ML algorithms [12].”