Towards Fine-grained Citation Function Classification

Abstract

We look into the problem of recogniz-ing citation functions in scientific liter-ature, trying to reveal authors ’ rationale for citing a particular article. We intro-duce an annotation scheme to annotate ci-tation functions in scientific papers with coarse-to-fine-grained categories, where the coarse-grained annotation roughly cor-responds to citation sentiment and the fine-grained annotation reveals more about ci-tation functions. We implement a Maxi-mum Entropy-based system trained on an-notated data under this scheme to auto-matically classify citation functions in sci-entific literature. Using combined lex-ical and syntactic features, our system achieves the F-measure of 67%. 1

Cite this work

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

Stored only when both papers are in the corpus — 31 citations exist upstream.

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