Last week we enjoyed a visit from Dr. Shiyan Ou from the School of Information Management, Nanjing University, China. The group enjoyed her visit and her seminar was very well received.
Title: Unsupervised Citation Sentence Identification based on Similarity Measurement
Abstract: Citation Context Analysis has obtained the interest of many researchers in the field of bibliometrics. To do this, the first step is to extract the context of each citation from a citing paper. We proposed a novel unsupervised approach for the identification of implicit citation sentences without attaching a citation tag. Our approach selects the neighbouring sentences around an explicit citation sentence as candidate sentences, calculates the similarity between a candidate sentence and a cited or citing paper, and deems those that are more similar to the cited paper to be implicit citation sentences. To calculate text similarity, we proposed four methods based on the Doc2vec model, the Vector Space Model (VSM) and the LDA model respectively. The experiment results showed that the hybrid method combing the probabilistic TF-IDF weighted VSM with the TF-IDF weighted Doc2vec obtained the best performance. Compared against other supervised methods, our approach does not need any annotated training corpus, and thus can be easy to apply to other domains in theory.
Our next visitor will be Professor Gloria Corpas Pastor who will be giving lectures on the 9th and 10th April.