Keyword extraction from a single document using word co-occurrence statistical information
Y. Matsuo, und M. Ishizuka. International Journal on Artificial Intelligence Tools, (2004)
Zusammenfassung
This paper explains a keyword extraction algorithm based solely on a single document. First, frequent terms are extracted. Co-occurrences of a term and frequent terms are counted. If a term appears frequently with a particular subset of terms, the term is likely to have important meaning. The degree of bias of the cooccurrence distribution is measured by the \# -measure. We show that our keyword extraction performs well without the need for a corpus. In this paper, a term is defined as a word or a word sequence. We do not intend to limit the meaning in a terminological sense. A word sequence is written as a phrase
%0 Journal Article
%1 matsuo_keyword_2004
%A Matsuo, Y
%A Ishizuka, M
%D 2004
%J International Journal on Artificial Intelligence Tools
%K terminologieextraktion
%P 2004
%T Keyword extraction from a single document using word co-occurrence statistical information
%V 13
%X This paper explains a keyword extraction algorithm based solely on a single document. First, frequent terms are extracted. Co-occurrences of a term and frequent terms are counted. If a term appears frequently with a particular subset of terms, the term is likely to have important meaning. The degree of bias of the cooccurrence distribution is measured by the \# -measure. We show that our keyword extraction performs well without the need for a corpus. In this paper, a term is defined as a word or a word sequence. We do not intend to limit the meaning in a terminological sense. A word sequence is written as a phrase
@article{matsuo_keyword_2004,
abstract = {This paper explains a keyword extraction algorithm based solely on a single document. First, frequent terms are extracted. Co-occurrences of a term and frequent terms are counted. If a term appears frequently with a particular subset of terms, the term is likely to have important meaning. The degree of bias of the cooccurrence distribution is measured by the \# -measure. We show that our keyword extraction performs well without the need for a corpus. In this paper, a term is defined as a word or a word sequence. We do not intend to limit the meaning in a terminological sense. A word sequence is written as a phrase},
added-at = {2018-11-04T17:02:36.000+0100},
author = {Matsuo, Y and Ishizuka, M},
biburl = {https://www.bibsonomy.org/bibtex/24d2d886adc1ed629b70e943e99f5e4c1/lepsky},
interhash = {05dd5f2b8e94d58f07b38ba577d6c689},
intrahash = {4d2d886adc1ed629b70e943e99f5e4c1},
journal = {International Journal on Artificial Intelligence Tools},
keywords = {terminologieextraktion},
pages = 2004,
timestamp = {2018-11-04T17:02:36.000+0100},
title = {Keyword extraction from a single document using word co-occurrence statistical information},
volume = 13,
year = 2004
}