Finding Implicit Citations in Scientific Publications : Improvements to Citation Context Detection Methods
Abstract: This thesis deals with the task of identifying implicit citations between scientiﬁc publications. Apart from being useful knowledge on their own, the citations may be used as input to other problems such as determining an author’s sentiment towards a reference, or summarizing a paper based on what others have written about it. We extend two recently proposed methods, a Machine Learning classiﬁer and an iterative Belief Propagation algorithm. Both are implemented and evaluated on a common pre-annotated dataset. Several changes to the algorithms are then presented, incorporating new sentence features, diﬀerent semantic text similarity measures as well as combining the methods into a single classiﬁer. Our main ﬁnding is that the introduction of new sentence features yield signiﬁcantly improved F-scores for both approaches.
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