Simple algorithms for complex relation extraction with applications to biomedical IE

Authors:
Ryan McDonald;Fernando Pereira;Seth Kulick;Scott Winters;Yang Jin;Pete White
Affiliations:
CIS, University of Pennsylvania, Philadelphia, PA;CIS, University of Pennsylvania, Philadelphia, PA;IRCS, University of Pennsylvania, Philadelphia, PA;Children's Hospital of Pennsylvania, Philadelphia, PA;Children's Hospital of Pennsylvania, Philadelphia, PA;Children's Hospital of Pennsylvania, Philadelphia, PA
Venue:
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Year:
2005

Citing 13
Cited 10

A maximum entropy approach to natural language processing

Computational Linguistics
An Algorithm that Learns What‘s in a Name

Machine Learning - Special issue on natural language learning
Algorithm 457: finding all cliques of an undirected graph

Communications of the ACM
Automatic labeling of semantic roles

Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Kernel methods for relation extraction

The Journal of Machine Learning Research
A novel use of statistical parsing to extract information from text

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Extracting the names of genes and gene products with a hidden Markov model

COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Chunking with support vector machines

NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An entity tagger for recognizing acquired genomic variations in cancer literature

Bioinformatics
Extracting gene pathway relations using a hybrid grammar: the Arizona Relation Parser

Bioinformatics
Classifying semantic relations in bioscience texts

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics

Mining relational data from text: From strictly supervised to weakly supervised learning

Information Systems
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
Learning field compatibilities to extract database records from unstructured text

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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
Extracting XML data from the web

Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Biomedical events extraction using the hidden vector state model

Artificial Intelligence in Medicine
Towards automatic pathway generation from biological full-text publications

IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Datasets for generic relation extraction*

Natural Language Engineering
Exploiting evidence from unstructured data to enhance master data management

Proceedings of the VLDB Endowment
Large-Scale learning of relation-extraction rules with distant supervision from the web

ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text.