In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Abstract

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.

Modeling the co-citation dependence on semantic layers of co-cited documents

“…Bichteler and Eaton, 1980; Eto, 2013; Gipp and Beel, 2009; Jeong et al , 2014). Research has also confirmed the CO-OP of co-citances in the co-citing documents (Yaghtin et al ., 2019a, b); thus, it is necessary to test how the phenomenon interacts with objective measures of the co-citation similarity in a single model. The CF is one of the frequently used co-citation measures compliant with IR systems' requirements in terms of effectiveness and efficiency.…”

“…Nobel-laureates-authored articles) (Yan et al ., 2020), improving traditional machine learning approaches for classification (Kilicoglu et al ., 2019) and ranking scientific papers (Gosh et al ., 2018). By analyzing the subjective contents of co-citances, Yaghtin et al (2019b) proposed a new feature called CO-OP. It calculates the similarity of co-cited papers based on the degrees of agreement between their co-citing items.…”

“…It calculates the similarity of co-cited papers based on the degrees of agreement between their co-citing items. It has been found effective in improving IR (Yaghtin et al ., 2019a, b). The measure is theoretically based on our existing knowledge confirming the subject relatedness of co-citations (Small, 1973; Bichteler and Eaton, 1980), as well as the predominant tendency of citers to give confirmatory citations and avoid negative credits (MacRoberts and MacRoberts, 1984; Athar and Teufel, 2012; Mahalakshmi et al , 2015; Sendhilkumar et al , 2013).…”

“…Aljuaid et al , 2020; Yan et al ., 2020; Kilicoglu et al ., 2019; Gosh et al ., 2018; Jia, 2018). By analyzing the opinions uttered in the co-citances, a new feature called co-opinionatedness (CO-OP) has been recently discovered by Yaghtin et al (2019b). It calculates the similarity of co-cited items based on the similarity of the opinions they receive from their co-citing paper(s) (Yaghtin et al ., 2019a, b).…”

“…By analyzing the opinions uttered in the co-citances, a new feature called co-opinionatedness (CO-OP) has been recently discovered by Yaghtin et al (2019b). It calculates the similarity of co-cited items based on the similarity of the opinions they receive from their co-citing paper(s) (Yaghtin et al ., 2019a, b). Accordingly, when document X is co-cited by documents Y and Z, the item receiving closer opinions to document X should bear more thematic relevance to it.…”