On the Expressive Power of Scientific Manuscripts

Abstract

Every research manuscript is appreciated in the form of citations. Citations are expected to carry the essence of the underlying base paper by some rhetorical means. However, this is not true in reality. Citation manipulations are equally possible which shall be identified using research semantics. This paper discusses machine learning based approaches for analyzing research citations with the aim of finding quality research citations. On analyzing the semantics of the research manuscript and the respective citations, this paper proposes various metrics for citation quality analysis including deep cite, raw expressive power, expressive power and normalized expressive power.

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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 — 7 citations exist upstream.

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