Abstract
A “term weighting” is a useful technique for keyword extraction and document classification. The traditional approach depends on high frequency terms, called positive weight ( PW) function. This paper presents a new weighting method that depends on low frequency terms, called negative weight ( NW) function. In this paper word similarity for typical verbs and objects is focused as an example for the application field. Negative weighted inverse verb frequency ( NWIVF) function is well defined in this study and new similarity measurement is presented by combining the NWIVF and PWIVF ( positive weighted inverse verb frequency) functions. The proposed method is applied to 11,000 relationships between verbs and nouns extracted from a large tagged corpus. By using this new method both recall and precision have improved by 33% and 18% respectively, over the positive weight method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
