Algorithm for Semantic Based Similarity Measure
نویسندگان
چکیده
In a document representation model the Semanti based Similarity Measure (SBSM), is proposed. This model combines phrases analysis as well as words analysis with the use of propbank notation as background knowledge to explore better ways of documents representation for clustering. The SBSM assigns semantic weights to both document words and phrases. The new weights reflect the semantic relatedness between documents terms and capture the semantic information in the documents. The SBSM finds similarity between documents based on matching terms (phrases and words) and their semantic weights. Experimental results show that the semantic based similarity Measure (SBSM) in conjunction with Propbank Notation has a promising performance improvement for text clustering.
منابع مشابه
A Novel Architecture for Detecting Phishing Webpages using Cost-based Feature Selection
Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy and system response time. The major time consumed by PWDS arises from feature extraction that ...
متن کاملA New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملA Semantic Similarity Measure between Sentences
The purpose of this paper is to present a mathematical model for estimating semantic similarity among sentences in texts. The similarity measure is constructed from the semantic similarity among concepts and a set of concepts. Based on this model, we develop algorithms to calculate the semantic similarity between two set of concepts and then the ones to estimate the semantic similarity between ...
متن کاملA Semantic approach for Text Clustering using WordNet based on Multi-Objective Genetic Algorithms
In this paper, we propose a method of MultiObjective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, for document clustering with semantic similarity measures based on WordNet. The MOGAs showed a high performance compared to other clustering algorithms. The main problem in the application of MOGAs for document clustering in the Vector Space Model (VSM) is that it ignores relationships between im...
متن کاملSentence Similarity Measuring by Vector Space Model Sentence Similarity Measuring by Vector Space Model
In Natural Language Processing and Text mining related works, one of the important aspects is measuring the sentence similarity. When measuring the similarity between sentences there are three major branches which can be followed. One procedure is measuring the similarity based on the semantic structure of sentences while the other procedures are based on syntactic similarity measure and hybrid...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013