Keywords: citation semantics, scholarly databases, citation modeling, classification
Abstract: The search for relevant information within large scholarly databases is becoming an unaffordable task where deeper semantic representations of citations could give impactful contributions. While some researchers have already proposed models and categories of citations, this often remains at a theoretical level only or it simply reduces the problem to a short-text classification of the context sentence. In this work, we propose "CiTelling": a radically new model of fine-grained semantic structures lying behind citational sentences able to represent their intent and features. After an extensive and multiple annotation of 1380 citations, we tested the validity and the reliability of the proposal through both qualitative and quantitative analyses. In particular, we were able to 1) extend the current depth of existing semantic representations when used in computational scenarios, 2) achieve high inter-annotation human agreements and 3) obtain state-of-the-art classification results with straightforward neural network models.
First Author Is Student: Yes
Subtrack: Science Data and Scholarly Communication