Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis

Abstract

Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem. One of the reason is two sentences can convey the same meaning with totally dissimilar words. This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences. Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures.

A Combination of Text Similarity And Latent Semantic Analysis (LSA) Methods In Automatic Scoring Essay

“…The combination of Text Similarity and Latent Semantic Analysis (LSA) methods can solve this problem [2]. On the use of text similarity method text will answer a series of processes to find keywords and two text answers are tested will be matched using Latent Semantic Analysis (LSA) [3], [4]. This research will be carried out the application design by comparing the similarity essay with an answer key that already existed in the system.…”