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Context Sensitive Topic Models for Author Influence in Document Networks ∗
"... In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors ’ interests. In this work, we propose to model the influence of cited authors along wi ..."
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Cited by 13 (4 self)
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In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors ’ interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. 1
Ranking Authors in Digital Libraries
"... Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document’s ..."
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Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document’s association with venues and citation links with other documents. We propose graph-based models that accommodate multiple sources of evidence in a PageRank-like algorithm for ranking experts. Our studies on two publiclyavailable datasets indicate that our model despite being general enough to be directly useful for ranking other types of objects performs on par with probabilistic models commonly used for expert ranking. 1.
Academic Network Analysis: A Joint Topic Modeling Approach
"... Abstract—We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network an ..."
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Abstract—We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Context Sensitive Topic Models for Author Influence in Document Networks
"... In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors ’ interests. In this work, we propose to model the influence of cited authors along wi ..."
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In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors ’ interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. 1
IEEE TRANSACTIONS ON CYBERNETICS 1 Expertise Finding in Bibliographic Network: Topic Dominance Learning Approach
"... Expert finding problem in bibliographic networks has received increased interests in recent years. This problem concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose two discrim ..."
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Expert finding problem in bibliographic networks has received increased interests in recent years. This problem concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose two discriminative methods to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from Microsoft academic search engine show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics.
1 Research dynamics, impact, and dissemination: A topic-level analysis
"... In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal-based assessments. The goal of this paper is to highlight topics as a valuable unit of ..."
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In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal-based assessments. The goal of this paper is to highlight topics as a valuable unit of analysis. A set of topic-based approaches is applied to a data set on library and information science publications. Results show that topic-based approaches are capable of revealing the research dynamics, impact, and dissemination of the selected data set. The paper also identifies a non-significant relationship between topic popularity and impact, and argues for the need to use both variables in describing topic characteristics. Additionally, a flow map illustrates critical topic-level knowledge dissemination channels.
Picking the Amateur’s Mind – Predicting Chess Player Strength from Game Annotations
"... Results from psychology show a connection between a speaker’s expertise in a task and the lan-guage he uses to talk about it. In this paper, we present an empirical study on using linguistic evidence to predict the expertise of a speaker in a task: playing chess. Instructional chess litera-ture clai ..."
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Results from psychology show a connection between a speaker’s expertise in a task and the lan-guage he uses to talk about it. In this paper, we present an empirical study on using linguistic evidence to predict the expertise of a speaker in a task: playing chess. Instructional chess litera-ture claims that the mindsets of amateur and expert players differ fundamentally (Silman, 1999); psychological science has empirically arrived at similar results (e.g., Pfau and Murphy (1988)). We conduct experiments on automatically predicting chess player skill based on their natural lan-guage game commentary. We make use of annotated chess games, in which players provide their own interpretation of game in prose. Based on a dataset collected from an online chess forum, we predict player strength through SVM classification and ranking. We show that using textual and chess-specific features achieves both high classification accuracy and significant correlation. Finally, we compare our findings to claims from the chess literature and results from psychology. 1
