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Showing 1 to 4 of 4 for “"named entity extraction"”.

  1. Extracting information from informal communication

    … for sub-problems of the information extraction task. We make contributions to the problems of named entity extraction, co-reference resolution and context tracking. We channel our efforts toward methods which are particularly applicable to informal communication. We also consider a …

    mit Repository record for Extracting information from informal communication (opens in a new tab)

  2. Extracting fields from free-text

    The Field Extraction Library (FEL) provides functions for named-entity extraction within free text. FEL models the content structure of the specified named-entities rather than relying on brittle, context-specific separator logic. Users specify the names of the fields they wish to extract, which …

    mit Repository record for Extracting fields from free-text (opens in a new tab)

  3. Knowledge extraction from unstructured data and classification through distributed ontologies

    … mainly consists in three steps: \emph{i)} the extraction from the text of atomic pieces of information, called named entities; \emph{ii)} the classification of these pieces of information through ontologies; \emph{iii)} the disambigation of them through Uniform Resource Identifiers (URIs) …

    poli-torino Repository record for Knowledge extraction from unstructured data and classification through distributed ontologies (opens in a new tab)

  4. Using Concept Maps as a Tool for Cross-Language Relevance Determination

    Concept maps, introduced by Novak, aid learners' understanding. I hypothesize that concept maps also can function as a summary of large documents, e.g., electronic theses and dissertations (ETDs). I have built a system that automatically generates concept maps from English-language ETDs in the …

    vt Repository record for Using Concept Maps as a Tool for Cross-Language Relevance Determination (opens in a new tab)