Learning Neural Textual Representations for Citation Recommendation

Kieu, Binh Thanh; Unanue, Iñigo Jauregi; Pham, Son Bao; Phan, Xuan-Hieu; Piccardi, Massimo

doi:10.1109/icpr48806.2021.9412725

2020 25th International Conference on Pattern Recognition (ICPR) 2021

DOI: 10.1109/icpr48806.2021.9412725

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Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu

Iñigo Jauregi Unanue

Son Bao Pham

et al.

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Cited by 3 publications

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ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

Degha,

LAALLAM

2024

ACM Trans. Manage. Inf. Syst.

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Navigating the ever-expanding sea of scientific literature presents a daunting challenge for researchers seeking relevant and up-to-date information. Traditional citation recommendation systems, while well-intentioned, often fall short due to their limited focus on text-based features and lack of contextual awareness. In this paper we introduce the ICA-CRMAS (Intelligent Context-Aware Approach for Citation Recommendation based on Multi-Agent System), an intelligent system that leverages the power of deep learning, semantic analysis, and multimodal learning to overcome these limitations. ICA-CRMAS goes beyond the surface, delving into the rich tapestry of information within academic papers, including figures, which often hold vital contextual clues. By weaving this contextual data directly into its recommendation models, ICA-CRMAS generates highly personalized and relevant suggestions. This comprehensive approach unlocks enhanced accuracy, diversity, and serendipity, enabling researchers to effectively discover papers aligning with their interests and research objectives. ICA-CRMAS illuminates its reasoning. Instead of opaque suggestions, the system provides clear explanations that justify and illustrate recommended citations. This transparency builds user confidence, allowing researchers to critically engage with and trust the system’s recommendations. Evaluation experiments conducted on real-world academic datasets demonstrate that ICA-CRMAS outperforms existing approaches across various metrics. it surpassing its closest competitor by a margin of 7.53 on accuracy, 6.07% on MRR and by 5.87 on Recall. User feedback further reinforces its effectiveness, with an Overall System Usability Scale (SUS) score of 76.73, exceeding benchmark scores for comparable systems.

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ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

Degha,

LAALLAM

2024

ACM Trans. Manage. Inf. Syst.

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Similarity Calculation via Passage-Level Event Connection Graph

2022

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Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.

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Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

et al. 2023

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Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Learning Neural Textual Representations for Citation Recommendation

Cited by 3 publications

References 20 publications

ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

Similarity Calculation via Passage-Level Event Connection Graph

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

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