Extracting Biochemical Interactions from MEDLINE Using a Link Grammar Parser

Authors:
Jing Ding;Daniel Berleant;Jun Xu;Andy W. Fulmer
Affiliations:
-;-;-;-
Venue:
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Year:
2003

Citing 0
Cited 16

Approaches to text mining for clinical medical records

Proceedings of the 2006 ACM symposium on Applied computing
Methodological Review: Extracting interactions between proteins from the literature

Journal of Biomedical Informatics
BioPPIExtractor: A protein-protein interaction extraction system for biomedical literature

Expert Systems with Applications: An International Journal
Analysis of link grammar on biomedical dependency corpus targeted at protein-protein interactions

JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts

BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Molecular event extraction from link grammar parse trees

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
On the unification of syntactic annotations under the stanford dependency scheme: a case study on BioInfer and GENIA

BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
IntEx: a syntactic role driven protein-protein interaction extractor for bio-medical text

ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts

LNLBioNLP '06 Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology
BioPPISVMExtractor: A protein-protein interaction extractor for biomedical literature using SVM and rich feature sets

Journal of Biomedical Informatics
Semantic relations for problem-oriented medical records

Artificial Intelligence in Medicine
A robust linguistic platform for efficient and domain specific web content analysis

Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Relation-Based document retrieval for biomedical literature databases

DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Collaborative curation of data from bio-medical texts and abstracts and its integration

DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
Relation-Based document retrieval for biomedical IR

Transactions on Computational Systems Biology V
An automated multi-component approach to extracting entity relationships from database requirement specification documents

NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many natural language processing approaches at various complexity levels have been reported for extracting biochemical interactions from MEDLINE. While some algorithms using simple template matching are unable to deal with the complex syntactic structures, others exploiting sophisticated parsing techniques are hindered by greater computational cost. This study investigates link grammar parsing for extracting biochemical interactions. Link grammar parsing can handle many syntactic structures and is computationally relatively efficient. We experimented on a sample MEDLINE corpus. Although the parser was originally developed for conversational English and made many mistakes in parsing sentences from the biochemical domain, it nevertheless achieved better overall performance than a co-occurrence-only method. Customizing the parser for the biomedical domain is expected to improve its performance further.