Towards the automatic identification of the nature of citations
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This document summarizes a research paper that aims to automatically identify the nature or function of citations in scientific articles. The goal is to design a method that can infer the author's reason for citing a given article. The researchers implemented a tool called CiTalO that takes citation sentences as input and outputs the most likely CiTO type based on techniques like ontology learning, word sense disambiguation, and sentiment analysis. An evaluation on a sample of manually annotated citations showed the most common citation function was "citesForInformation" and no single configuration of CiTalO emerged as clearly best.
![Result
Similarly to Teufel et al. [19] the most
neutral CiTO property,
citesForInformation, was the most
prevalent function in our dataset too,
as the second most used property
was usedMethodIn
We run CiTalO on the same sample according to 8 different configurations and we
compared the results with humans annotations
No configuration
that emerges as
the absolutely best
one from these
data
Worst
configurations were
those that took into
account all the
proximal synsets
We asked humans to manually annotate 106 citation sentences, contained in scientific ar
according to CiTO properties](https://image.slidesharecdn.com/citalo-130601114448-phpapp02/85/Towards-the-automatic-identification-of-the-nature-of-citations-5-320.jpg)
Towards the automatic identification of the nature of citations
- 1. Towards the automatic identification of the nature of citations (1) Department of Computer Science and Engineering, University of Bologna, Italy (2) STLab-ISTC, National Research Council, Italy 30 May 2013 Montpellier, France ESWC 2013
- 2. Motivation • Bibliographic citations can be seen as tools for: – linking research: making pointers to related works, to source of experimental data, to methods used, etc. – disseminating research: conference proceedings, journals, Web platforms (e.g. blogs, wikis), Semantic Publishing platforms and projects (e.g. OpenCitation, OpenBibliography, Lucero) – exploring research: new ways of browsing article through networks of citations (e.g. CiteWiz, Citation Sensitive In-browser Summariser) – evaluating research: measuring the importance of journals (e.g. impact factor) or the scientific productivity of authors (e.g. h-index) • Assumption: all these activities can be radically improved by exploiting the actual function of citations, i.e. author’s reason for citing a given paper
- 3. Goal • To design a method able to automatically infer the author’s reason for citing a scientific article • To implement a tool that is comparable to humans in the task of identifying the nature of citations
- 4. Available online at http://wit.istc.cnr.it:8080/tools/citalo It extends the research outlined in earlier work X. Ontology learning Citation type extraction Word-sense disambiguation Alignment to CiTO Sentiment analysis Output: cito:extends Input: a sentence containing a reference to a bibliographic entity indicated by an “X” Derive a logical (i.e. an OWL ontology) representation of the sentence through FRED Extract candidate types for the citation by looking for patterns in FRED output via SPARQL Gather the sense of the candidate types through IMS with respect to OntoWordNet Capture the sentiment polarity emerging from th text through AlchemyAPI Assign CiTO types to the citation through SPARQL CONSTRUCT
- 5. Result Similarly to Teufel et al. [19] the most neutral CiTO property, citesForInformation, was the most prevalent function in our dataset too, as the second most used property was usedMethodIn We run CiTalO on the same sample according to 8 different configurations and we compared the results with humans annotations No configuration that emerges as the absolutely best one from these data Worst configurations were those that took into account all the proximal synsets We asked humans to manually annotate 106 citation sentences, contained in scientific ar according to CiTO properties
- 6. Thanks