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In order to over the shortcoming of the incomprehensive of summarization, a new lexical-chain-based keywords extraction and automatic summarization algorithm from Chinese texts based on the unknown word recognition using co-occurrence of neighbor words is proposed in this paper, and an algorithm for constructing
Keyword extraction has been a very traditional topic in Natural Language Processing. However, most methods have been too complicated and slow to be applied in real applications, for example in web-based system. This paper proposes an approach which will complete some preparing works focusing on exploring the
Document Summarization (ADS) systems are suitable for the task of outlining useful data. The ADS system model takes a text document as input, and outputs a semantically-relevant summary of this information. This information can be further separated and outlined as keywords, or keyphrases. This paper proposes a novel
The complex network theory is widely used in the field of keyword extraction. Through analyzing the insufficient of keyword extraction algorithms using traditional complex network, this paper proposes a new method to extract Chinese keyword based on semantically weighted network. On the basis of K-nearest neighbor
Keywords Extraction plays a very important role in the text mining domain, since the keywords can represent the asserted main point in a document. Based on term network and deleting actor index, an effective keywords extraction algorithm is proposed to extract high frequent terms as well as important terms with low
Keywords Extraction plays a very important role in the text-mining domain, since the keywords can represent the asserted main point in a document. Based on the term network and deleting actor index, an effective keywords extraction algorithm is proposed to extract high frequent terms as well as important terms with
In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain
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