BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature - Wikidata - awgldk - TriplyDB

BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

http://www.wikidata.org/entity/Q30951526

about

Weakly supervised learning of biomedical information extraction from curated data Allie: a database and a search service of abbreviations and long forms Evaluation and cross-comparison of lexical entities of biological interest (LexEBI)AuDis: an automatic CRF-enhanced disease normalization in biomedical text Towards a career in bioinformatics Finding abbreviations in biomedical literature: three BioC-compatible modules and four BioC-formatted corpora.BioC interoperability track overview Machine learning with naturally labeled data for identifying abbreviation definitions Soft tagging of overlapping high confidence gene mention variants for cross-species full-text gene normalization.Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.Detecting abbreviations in discharge summaries using machine learning methods.Collaborative relation annotation and quality analysis in Markyt environment.

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P2860

BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

http://www.wikidata.org/entity/Q30951526

description

2009 nî lūn-bûn

@nan

2009 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2009 թվականի դեկտեմբերին հրատարակված գիտական հոդված

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2009年の論文

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2009年学术文章

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2009年学术文章

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2009年学术文章

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2009年学术文章

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2009年学术文章

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2009年學術文章

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name

BIOADI: a machine learning app ...... tions in biological literature

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BIOADI: a machine learning app ...... tions in biological literature

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BIOADI: a machine learning app ...... tions in biological literature

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BIOADI: a machine learning app ...... tions in biological literature

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BMC Bioinformatics

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BIOADI: a machine learning app ...... tions in biological literature

@en

P2093

Cheng-Ju Kuo

http://www.w3.org/2001/XMLSchema#string

Chun-Nan Hsu

http://www.w3.org/2001/XMLSchema#string

Kuan-Ting Lin

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Maurice H T Ling

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P2860

Overview of the protein-protein interaction annotation extraction task of BioCreative II

Tools for loading MEDLINE into a local relational database

BioCreAtIvE task 1A: gene mention finding evaluation

Literature mining for the biologist: from information retrieval to biological discovery

Playing biology's name game: identifying protein names in scientific text.

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

Exploring the boundaries: gene and protein identification in biomedical text

Recognition of protein/gene names from text using an ensemble of classifiers.

A comparison study on algorithms of detecting long forms for short forms in biomedical text

Identification of transcription factor contexts in literature using machine learning approaches.

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1471-2105-10-s15-s7

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10.1186/1471-2105-10-S15-S7

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10 Suppl 15

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2009-12-03T00:00:00Z

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machine learning

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