Neural MultiTask Learning for Citation Function and Provenance Citation function and provenance are two cornerstone tasks in citation analysis. Given a citation, the former task determines its rhetorical role, while the latter locates the text in the cited paper that contains the relevant cited information. We hypothesize that these two tasks are synergistically related, and build a model that validates this claim. For both tasks, we show that a singlelayer convolutional neural network (CNN) is able to surpass the performance of existing stateoftheart baselines. More importantly, we show that the two tasks are indeed synergistic: by training both tasks in one go using multitask learning, we demonstrate additional performance gains in both tasks. Altogether, our contributions outperform the current stateofthearts by 2% and 7%, with statistical significance for citation function and citation provenance prediction tasks, respectively.

Determining Citation Blocks using Endtoend Neural Coreference Resolution Model for Citation Context Analysis The study of citation contexts is an important element in understanding the function of citations and categorizing the relationships between works. One of the problems in this field is defining the size of citation contexts. In this paper, we propose the definition of citation blocks (CB) that are citation contexts composed of one or more sentences that are linked by coreference clusters. We describe the methodology for the automatic processing and determining the boundaries of CB and observe the different sizes of CB in the different sections of the IMRaD structure of articles. The results are obtained from a sample of 70,000 citation contexts extracted from the PLOS dataset.