An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

By Changqin Quan, M. Wang, Fuji Ren. 2014

83 citations recorded by the source corpus.

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein–protein interactions extraction, and (2) Gene–suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene–suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Xiao Sun
6. Yuan Luo
7. Lei Hua
8. Chanqin Quan

In Molecular Biology

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Yuan Luo
6. Lei Hua
7. Chanqin Quan
8. Zhiyong Lu

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[144] applied kernel-based pattern clustering and sentence parsing for interaction identi-”
• Multichannel Convolutional Neural Network for Biological Relation Extraction (2016, background): “Different from DDI task, PPI task aims to extract the interaction relations among proteins, and it has captured much interest among the study of biomedical relations recently [1, 2].”
• A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction (2016, unknown): “Research Article”