Citation Analysis with Neural Attention Models

By Tsendsuren Munkhdalai, John P. Lalor, Hong Yu. 2016

22 citations recorded by the source corpus.

Automated citation analysis (ACA) can be important for many applications including author ranking and literature based information retrieval, extraction, summarization and question answering. In this study, we developed a new compositional attention network (CAN) model to integrate local and global attention representations with a hierarchical attention mechanism. Training on a new benchmark corpus we built, our evaluation shows that the CAN model performs consistently well on both citation classification and sentiment analysis tasks.

Subfields: Artificial Intelligence

Citations in the corpus

• Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “This suggests that architecture with more flexible pooling models of signals such as attentive pooling networks [61] and neural attention models [27] may lead to more accurate relation classification, which will be our future work.”