Wei Wang

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It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where(More)
Graph based sentence ranking algorithms such as PageRank and HITS have been successfully used in query-oriented summarization. With these algorithms, the documents to be summarized are often modeled as a text graph where nodes represent sentences and edges represent pairwise similarity relationships between two sentences. A deficiency of conventional graph(More)
We address the problem of unsupervised ensemble ranking in this paper. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned <i>combine-then-rank</i> and(More)
This paper describes our system for the NTCIR-7 Patent Mining Task which sought to make automatic text classification pragmatic. Our system employs an improved KNN algorithm which makes trade-off between effectiveness and time complexity. We have tried two distance metrics in our algorithm: cosine similarity and Euclid distance. Evaluation results on(More)