Investigating Citation Linkage as a Sentence Similarity Measurement Task using Deep Learning

Research publications reflect advancements in the corresponding research domain. In these research publications, scientists often use citations to bolster the presented research findings and portray the improvements that come with these findings, at the same time, to make the contents more understandable to the audience by navigating the flow of information. In the science domain, a citation refers to the document from where this information originates but doesn\u27t specify the text span that is actually being cited. A more precise reference would indicate the text being referenced. This thesis develops a framework which can create a linkage between the citing sentences from the ongoing research article and the related cited sentences from the corresponding referenced documents. This citation linkage problem has been modeled as a semantic relatedness task where given a citing sentence the framework pairs this citing sentence with each sentence from the reference document and then tries to determine which sentence pair is semantically similar and which pair is not. Construction of the citation linkage framework involves corpus creation and utilizing deep-learning models for semantic similarity measurement

Exploiting Semantic Similarity Between Citation Contexts For Direct Citation Weighting And Residual Citation

This study used the semantic similarity between citation contexts to develop one scheme for weighting direct citations, and another scheme for allocating residual citations to a publication from its nth citation generation level publication. A relationship between the new direct citation weighting scheme and each of five existing schemes was investigated while the new residual citation scheme was compared with the cascading citation scheme. Two datasets from biomedical publications were used for this study, one each for the direct and residual citation weighting aspects of the study. The sample for the direct citation aspect contained 100 publications that received 7317 citations, 11,234 citation contexts, and 9,795 citation context pairs. A sample of 981 citation context pairs was given to two human experts for annotation into “similar”, “somewhat similar”, and “not similar” classes. Semantic similarity scores between the 11,234 citation contexts were obtained using BioSent2Vec word-embedding model for biomedical publications. The residual citation aspect sample included ten base articles and five generations of citations from which 5272 citation context pairs were obtained. Results of the Spearman’s rank correlation test showed that the correlation coefficients between the proposed direct citation weighting scheme and each of the weighting schemes “number of positive sentiments,” “number of multiple citation mentions,” “sum of multiple citation mentions,” “number of citations,” and “number of citation mentions” were .83, .89, .89, .93, and .99 respectively. The average residual citations received from the 2nd, 3rd, 4th and 5th citation generation level papers were 0.47, 0.43, 0.40, and 0.37 respectively. These average residual citations were significantly different from the averages of 0.5, 0.25, 0.125, and 0.0625 suggested by the cascading citation scheme. Even though the proposed direct citation weighting scheme and the residual citation scheme require more complex computations, it is recommended that they should be considered as credible alternatives to the “number of citation mentions” and cascading citation scheme respectively