When carrying out scientific research, the first step is to acquire relevant papers. It is easy to grab vast numbers of papers by inputting a keyword into a digital library or an online search engine. However, reading all the retrieved papers to find the most relevant ones is agonizingly time-consuming. Previous works have tried to improve paper search by clustering papers with their mutual similarity based on reference relations, including limited use of the type of citation (e.g. providing background vs. using specific method or data). However, previously proposed methods only classify or organize the papers from one point of view, and hence not flexible enough for user or context-specific demands. Moreover, none of the previous works has built a practical system based on a paper database. In this paper, we first establish a paper database from an open-access paper source, then use machine learning to automatically predict the reason for each citation between papers, and finally visualize the resulting information in an application system to help users more efficiently find the papers relevant to their personal uses. User studies employing the system show the effectiveness of our approach.
Abstract.We have proposed an idea to predict citation-reasons between scientific papers with machine-learning techniques, and try to narrow down the search range for relevant papers based on the citation-reasons. However, the machine-learning method seems not accurate enough according to a subject experiment. In this paper, as a substitution of the machine-learning method, we have proposed a strategy to annotate citation-reasons between papers in a crowdsourcing manner. An analysis on the result has shown the effectiveness of our strategy and some future tasks.
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