The DECA (Disease Extraction with Concept Association)

By | September 22, 2011

The DECA (Disease Extraction with Concept Association) project
concerns automatically associating concepts to entity mentions in biomedical text (e.g., MEDLINE abstracts).
A considerable amount of research was put into lexical disambiguation of the biomedical names.
This is because a string of words often refers to different meanings depending on the context, hence causing ambiguity.
A more sensible way to organise information is by concepts, where a concept has unambiguous meaning and can be associated with a unique identifier.
To make text mining useful for the community of biological sciences, one crucial step is to link the hidden and ambiguous mentions of named entities in text to unique concepts in knowledge bases.

The approach to organism disambiguation in DECA is automatically identifying the species-indicating words (e.g., human) and biomedical named entities (e.g., protein P53) in text, and then judging whether the species-entity relations are positive, where a positive relation means that an entity belongs to the organism indicated by the species-indicating word. Natural lanauge syntactic parsers and machine learning techniques were applied to classify species-entity relations.

Name
The DECA (Disease Extraction with Concept Association)
Documentation
Protocol
REST
WSDL
Endpoint
http://www.nactem.ac.uk/UCompareWorkflows/DECA
Topic
Biology
Type
Tags
, , , , , , , , ,
Description

The DECA (Disease Extraction with Concept Association) project concerns automatically associating concepts to entity mentions in biomedical text (e.g., MEDLINE [...]

Further information

The DECA (Disease Extraction with Concept Association) project
concerns automatically associating concepts to entity mentions in biomedical text (e.g., MEDLINE abstracts).
A considerable amount of research was put into lexical disambiguation of the biomedical names.
This is because a string of words often refers to different meanings depending on the context, hence causing ambiguity.
A more sensible way to organise information is by concepts, where a concept has unambiguous meaning and can be associated with a unique identifier.
To make text mining useful for the community of biological sciences, one crucial step is to link the hidden and ambiguous mentions of named entities in text to unique concepts in knowledge bases.

The approach to organism disambiguation in DECA is automatically identifying the species-indicating words (e.g., human) and biomedical named entities (e.g., protein P53) in text, and then judging whether the species-entity relations are positive, where a positive relation means that an entity belongs to the organism indicated by the species-indicating word. Natural lanauge syntactic parsers and machine learning techniques were applied to classify species-entity relations.

Original source
BioCatalogue

1 Comment

joselu on 2011/12/21 at 1:35 pm.

Take a look to this biomedical text annotator:
http://biolabeler.com

bioLabeler extracts UMLS concepts from free text filtering those concepts by semantic type or source dictionary,

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