Verb selection using semantic role labeling for citation classification

Abstract

Citation classification is the task of assigning a category to a reference or citation. The current sets of categories or classes proposed in the literature vary in size and they are based on the analysis of a small sample of citation sentences. We are developing a process to automatically generate such categories and base them on the analysis of a large corpus of papers. Part of the generation process involves selecting the main verb relevant to the reference being cited in the sentence. In this paper we present our recently developed technique that automatically identifies the relevant verb in a citation sentence. The technique uses heuristic rules, which are dependent on the results of a semantic role labeler. Four test sets were collected, and the common annotations of the test sets annotated by three people were used to assess the accuracy of the rules. Through experimentation we show that the average accuracy achieved using our technique that automatically extracts verbs from citation sentences across the four test sets is reasonable at 75%.

A New Scheme for Citation Classification based on Convolutional Neural Networks

“…Moreover, authors in [18] explored these issues by selecting the relevant verb in a citation sentence. They labeled each citation sentence by using semantic role labeling and then proposed six rules to extract and select the best verb that represents the citation sentence.…”