Research on Web Page Classification Method Based on Query Log

Web page classification is an important application in many fields of Internet information retrieval, such as providing directory classification and vertical search. Methods based on query log which is a light weight version of Web page classification can avoid Web content crawling, making it relatively high in efficiency, but the sparsity of user click data makes it difficult to be used directly for constructing a classifier. To solve this problem, we explore the semantic relations among different queries through word embedding, and propose three improved graph structure classification algorithms. To reflect the semantic relevance between queries, we map the user query into the low-dimensional space according to its query vector in the first step. Then, we calculate the uniform resource locator (URL) vector according to the relationship between the query and URL. Finally, we use the improved label propagation algorithm (LPA) and the bipartite graph expansion algorithm to classify the unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other Web page classification methods based on query log.     

Automatic Multi-Document Summarization Based on Keyword Density and Sentence-Word Graphs

As a fundamental and effective tool for document understanding and organization,multi-document summarization enables better information services by creating concise and informative reports for large collections of documents.In this paper,we propose a sentence-word two layer graph algorithm combining with keyword density to generate the multi-document summarization,known as Graph Keywordρ.The traditional graph methods of multi-document summarization only consider the influence of sentence and word in all documents rather than individual documents.Therefore,we construct multiple word graph and extract right keywords in each document to modify the sentence graph and to improve the significance and richness of the summary.Meanwhile,because of the differences in the words importance in documents,we propose to use keyword density for the summaries to provide rich content while using a small number of words.The experiment results show that the Graph Keywordρ method outperforms the state of the art systems when tested on the Duc2004 data set.