Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection

Abstract

Text mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text mining. In this paper, we propose a novel approach of feature selection for entity extraction that exploits the concept of deep learning and Particle Swarm Optimization (PSO). The system utilizes word embedding features along with several other features extracted by studying the properties of the datasets. We obtain an interesting observation that compact word embedding features as determined by PSO are more effective compared to the entire word embedding feature set for entity extraction. The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG and AiMed. The effectiveness of the proposed approach is evident with significant performance gains over the baseline models as well as the other existing systems. We observe improvements of 7.86%, 5.27% and 7.25% F-measure points over the baseline models for GE-NIA, GENETAG, and AiMed dataset respectively.

Towards a Seamless Integration of Word Senses into Downstream NLP Applications

“…Interestingly, unlike for the other two datasets, the introduction of pre-trained word embeddings to the system results in reduced performance on Ohsumed. This suggests that general domain embeddings might not be beneficial in specialized domains, which corroborates previous findings by Yadav et al (2017) on a different task, i.e. entity extraction.…”

A semi-supervised approach for extracting TCM clinical terms based on feature words

“…For example, Ahmed et al [ 20 ] and Lei et al [ 21 ] use SVM, KNN, DT, SVM to extract the named entities from biomedical corpus and Chinese clinical text. Shweta et al [ 22 ] apply PSO (Particle Swarm Optimization) model for feature selection in NER research. Nowadays, the most popular methods have also been turned into using deep learning frameworks, including active learning [ 23 ], DNN (deep neural networks)-CRF [ 24 ] or LSTM-CRF [ 25 – 27 ].…”

A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics

“…and Priego, 2017) and sentence similarity detection . In terms of the biomedical domain, word embedding-based features have been used for entity extraction in biomedical corpora (Yadav et al, 2017) or clinical information extraction (Kholghi et al, 2016). Several approaches for personal health mention classification have been reported (Aramaki et al, 2011;Lamb et al, 2013a;Yin et al, 2015).…”