Keretna et al., 2014 - Google Patents

Classification ensemble to improve medical named entity recognition

Keretna et al., 2014

Document ID
1638872775816433245
Author
Keretna S
Lim C
Creighton D
Shaban K
Publication year
Publication venue
2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

External Links

Snippet

An accurate Named Entity Recognition (NER) is important for knowledge discovery in text mining. This paper proposes an ensemble machine learning approach to recognise Named Entities (NEs) from unstructured and informal medical text. Specifically, Conditional Random …
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Classifications

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    • G06F17/30634Querying
    • G06F17/30657Query processing
    • G06F17/30675Query execution
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    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
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    • G06F17/27Automatic analysis, e.g. parsing
    • G06F17/2765Recognition
    • G06F17/2775Phrasal analysis, e.g. finite state techniques, chunking
    • G06F17/278Named entity recognition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
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    • G06F17/2765Recognition
    • G06F17/277Lexical analysis, e.g. tokenisation, collocates
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    • G06COMPUTING; CALCULATING; COUNTING
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    • G06F17/2795Thesaurus; Synonyms
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    • G06F17/30731Creation of semantic tools
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
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