Ensemble-style Self-training on Citation Classification

Abstract

Classification of citations into categories such as use, refutation, comparison etc. may have several relevant applications for digital libraries such as paper browsing aids, reading recommendations, qualified citation indexing, or fine-grained impact factor calculation. Most citation classifi-cation approaches described so far heavily rely on rule systems and patterns tailored to specific science domains. We focus on a less manual approach by learning domain-insensitive features from textual, physical, and syntactic aspects. Our experiments show the effectiveness of this feature set with various machine learning algorithms on datasets of different sizes. Further-more, we build an ensemble-style self-training classification model and get bet-ter classification performance using only few training data, which largely reduces the manual annotation work in this task. 1

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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 — 82 citations exist upstream.

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