Digital Library

Abstract

We develop SolutionTailor, a novel system that recommends papers that provide diverse solutions for a specific research objective. The proposed system does not require any prior information from a user; it only requires the user to specify the target research field and enter a research abstract representing the user’s interests. Our approach uses a neural language model to divide abstract sentences into “Background/Objective” and “Methodologies” and defines a new similarity measure between papers. Our current experiments indicate that the proposed system can recommend literature in a specific objective beyond a query paper’s citations compared with a baseline system.

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Reference

If you use our dataset, please refer to the following paper.

Tetsuya Takahashi and Marie Katsurai, “SolutionTailor: Scientific paper recommendation based on fine-grained abstract analysis,” Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham, 2022.

Abstract

We present a novel tool that finds the relevance between publication venues to foster opportunities for collaboration development. When a user inputs a publication venue name related to the user’s research field, our tool first shows several relevant publication venues using results of citation network analysis. After the user selects one of those, our tool shows the trend information for each venue as well as the common keywords between the two venues.

Demo video

Reference

If you use our lexicons, please refer to the following paper.

M. Kimura and M. Katsurai, “Automatic Construction of an Emoji Sentiment Lexicon,” Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 1033–1036, 2017.

Abstract

Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. TrendNets is a novel visualization approach that highlights the rapid changes in edge weights over time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics.

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Reference

If you use our code, please refer to the following paper.

M. Katsurai and S. Ono, “Mapping Emerging Research Trends from Dynamic Co-Word Networks via Sparse Representations,”Scientometrics, vol. 121, no. 3, pp. 1583–1598, 2019.