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This paper presents an automatic term extraction method based on Markov process. The method aims to extract multi-word domain terms from English corpora. The paper proves that the extracting term process is a Markov chain firstly, and then gives the steps of the Markov-based method. In order to evaluate our method, we use a corpus related to computer science got by Web crawlers, and extract domain...
A new method to compute the similarity of two blog posts is proposed in this paper. This method mainly has two parts including keywords extraction and semantic similarity measurement. During keywords extraction part, the method utilizes particular post features to extract keywords from one blog post with the aim to improve the correlation rate. In order to compute the similarity of any two blog posts...
Information Content (IC) is an important dimension of assessing the semantic similarity between two terms or word senses in word knowledge. The conventional method of obtaining IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with actual usage in text as derived from a large corpus. In this paper, a new model of IC is presented, which relies on...
Local context analysis is a main way to enhance the effectiveness of query expansion in the information retrieval field. A typical query may go through a pre- refinement process to improve its retrieval power. Most of the existing local context analysis methods are attempting to solve invalid selection of additive terms, which will result in retrieval performance degradation, in the process of query...
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