@article{
 title = {Automatic Keyword Extraction Using Domain Knowledge},
 type = {article},
 year = {2001},
 identifiers = {[object Object]},
 pages = {472-482},
 volume = {2004},
 websites = {http://www.springerlink.com/index/M20AV0YUMKMGETLA.pdf},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 editors = {[object Object]},
 id = {c2cb7930-8f1c-3f00-bf48-aa1f12cbf524},
 created = {2012-02-28T00:51:15.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {keyword extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Hulth2001},
 private_publication = {false},
 abstract = {Documents can be assigned keywords by frequency analysis of the terms found in the document text, which arguably is the primary source of knowledge about the document itself. By including a hierarchically organised domain specific thesaurus as a second knowledge source the quality of such keywords was improved considerably, as measured by match to previously manually assigned keywords. In the presented experiment, the combination of the evidence from frequency analysis and the hierarchically organised thesaurus was done using inductive logic programming.},
 bibtype = {article},
 author = {Hulth, Anette and Karlgren, Jussi and Jonsson, Anna and Bostrom, Henrik and Asker, Lars},
 journal = {English}
}

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