An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

Quan, Changqin; Wang, Meng; Ren, Fuji

doi:10.1371/journal.pone.0102039

Cited by 73 publications

(41 citation statements)

References 22 publications

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“…Various machine learning-based methods including supervised machine learning methods (30, 31), pattern clustering (32) and topic modeling (33) were used before deep learning models became dominant among the recent advances. Besides conventional DNN models (34, 35), dependency (15, 36) and character level (16) information have been used to enhance the models with improvement over their baselines.…”

Section: Related Workmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical–protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical–protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.

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Section: Related Workmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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“…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. This approach has been applied on two different tasks which are protein-protein interactions extraction, and genesuicide association extraction [15].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Extracting Biological Relations from Different Resources

Houby¹

2015

IJCA

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The World Wide Web provides a vast source of information of almost all types. Biological data specifically have increased dramatically in the past years because of the exponential growth of knowledge in biological domain. It is very difficult to search for the required data in unstructured documents. Text documents often hide valuable structured data. This data can be exploited if available as a relational table that could be used to answer queries or to perform data mining tasks. Manually extracting biological relations from published literature and transforming them into machine-understandable knowledge is a difficult task because biological domain comprises huge, dynamic, and complicated knowledge. Automatic extraction of semantic relation between biological terms from unstructured documents is challenging in information extraction and important task for deep information processing and management.In this research, a framework has been developed to extract different relations between various biological entities from documents. Semi supervised approach has been used to develop the framework. It requires the user to just provide a handful of valid pairs as initial seeds of the target relation, with no other training. Different patterns can be generated from initial seeds, and then from these patterns additional relation pairs can be extracted. The results has showed that different relations can be extracted such as gene-disease, protein-protein.

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“…Therefore, it is significant to further understand the interactions of drugs to reduce drug-safety accidents. 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]. There are a number of databases which have been created for DDI (DrugBank [3, 4]) and PPI (MINT [5], IntAct [6]).…”

Section: Introductionmentioning

confidence: 99%

Multichannel Convolutional Neural Network for Biological Relation Extraction

Quan

Hua

Sun

et al. 2016

BioMed Research International

120

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The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.

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An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

Cited by 73 publications

References 22 publications

Extracting chemical–protein relations using attention-based neural networks

Extracting chemical–protein relations using attention-based neural networks

A Framework for Extracting Biological Relations from Different Resources

Multichannel Convolutional Neural Network for Biological Relation Extraction

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