Citation Analysis with Neural Attention Models

Abstract

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.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 18 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.