Textual Analysis of ICALEPCS and IPAC Conference Proceedings: Revealing Research Trends, Topics, and Collaborations for Future Insights and Advanced Search
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Abstract (arXiv)
In this paper, we show a textual analysis of past ICALEPCS and IPAC conference proceedings to gain insights into the research trends and topics discussed in the field. We use natural language processing techniques to extract meaningful information from the abstracts and papers of past conference proceedings. We extract topics to visualize and identify trends, analyze their evolution to identify emerging research directions, and highlight interesting publications based solely on their content with an analysis of their network. Additionally, we will provide an advanced search tool to better search the existing papers to prevent duplication and easier reference findings. Our analysis provides a comprehensive overview of the research landscape in the field and helps researchers and practitioners to better understand the state-of-the-art and identify areas for future research.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 13 Oct 2023.
Builds on
7 of 21 references.
References · 7
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2020Neural Topic Modeling by Incorporating Document Relationship Graphcited by 13
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Citing papers per year: 2024: 1.
1 paper.