Integrating Word Sequences and Dependency Structures for Chemical-Disease Relation Extraction
نویسندگان
چکیده
Understanding chemical-disease relations (CDR) from biomedical literature is important for biomedical research and chemical discovery. This paper uses a k-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intraand inter-sentence level chemical-disease relations, which are merged as the final CDR. Experiments on the BioCreative V CDR dataset show that both word sequences and dependency structures are effective for CDR extraction, and their integration could further improve the extraction performance.
منابع مشابه
Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such representations in the automated relation extraction from text. We process the above data by mea...
متن کاملEnd-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional treestructured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a sin...
متن کاملExploiting graph kernels for high performance biomedical relation extraction
BACKGROUND Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Pa...
متن کاملExtracting Spatial Entities and Relations in Korean Text
A spatial information extraction system retrieves spatial entities and their relationships for geological searches and reasoning. Spatial information systems have been developed mainly for English text, e.g., through the SpaceEval competition. Some of the techniques are useful but not directly applicable to Korean text, because of linguistic differences and the lack of language resources. In th...
متن کاملKernel approaches for genic interaction extraction
MOTIVATION Automatic knowledge discovery and efficient information access such as named entity recognition and relation extraction between entities have recently become critical issues in the biomedical literature. However, the inherent difficulty of the relation extraction task, mainly caused by the diversity of natural language, is further compounded in the biomedical domain because biomedica...
متن کامل