Machine Learning
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Semantic Role Labeling
SRL-based verb selection for ESL
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A high-performance syntactic and semantic dependency parser
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
Hi-index | 0.00 |
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%.