Citation function, polarity and influence classification
Citations are a valuable resource for characterizing scientific publications that has already been used in applications such as summarization and information retrieval. These applications could be even better served by expanding citation information. We aim to achieve this by extracting and classifying citation information from the text, so that subsequent applications may make use of it. We make three contributions to the advancement of fine-grained citation classification. First, our work uses a standard classification scheme for citations that was developed independently of automatic classification and therefore is not bound to any particular citation application. Second, to address the lack of available annotated corpora and reproducible results for citation classification, we are making available a manually-annotated corpus as a benchmark for further citation classification research. Third, we introduce new features designed for citation classification and compare them experimentally with previously proposed citation features, showing that these new features improve classification accuracy.