Measurement of Document Similarity using Term/Term-pair Features and Neural Network
김혜숙, Parksangchul, Soo-Hyung Kim · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2004
This paper proposes a method for measuring document similarity between two documents. One of the most significant ideas of the method is to estimate the degree of similarity between two documents based on the frequencies of terms and term-pair, existing in both the two documents. In contrast to conventional methods which takes only one feature into account, the proposed method considers several features at the same time and meatures the similarity using a neural network. To prove the superiority of our method, two experiments have been conducted. One is to verify whether the two input documents are from the same document or not. The other is a problem of information retrieval with a document as the query against a large number of documents. In both the two experiments, the proposed method shows higher accuracy than two conventional methods, Cosine similarity measurement and a term-pair method.