Abstract
In method of Semantic similarity calculating, the major is based on VSM(Vector Space Model).It has aroused significant research attention in recent years due to its advantage in topic tracking. In this paper a modified VSM, namely Semantic Vector Space Model, is put forward. To establish the model, numerous lexical chains based on HowNet are first built, then sememes of the lexical chains are extracted as characteristics of feature vectors. Afterwards, initial weight and structural weight of the characteristics are calculated to construct the Semantic Vector Space Model, encompassing both semantic and structural information. The initial weight is collected from word frequency, while the structure weight is obtained from a designed calculation method. Finally, the model is applied in web news topic tracking with satisfactory experimental results, conforming the method to be effective and desirable.
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.
