This project investigates the convergence between Information Retrieval (IR) and Natural Language Processing (NLP) by analyzing publications from leading conferences (e.g., SIGIR, WSDM, ACL, EMNLP) over the past decade. The goal is to quantify how the two fields have become increasingly interconnected through shared tasks, methods, and research communities. The study will examine topic overlap (e.g., question answering, retrieval-augmented generation), methodological trends (e.g., transformers, embeddings), and citation patterns between the communities. By combining bibliometric analysis with content-based clustering of papers, the project aims to provide empirical evidence of the growing integration of IR and NLP, particularly in the era of large language models.

References

1. Dieng, A.B., Ruiz, F.J. and Blei, D.M., 2020. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8, pp.439-453. https://doi.org/10.1162/tacl_a_00325
2. BERTopic https://github.com/MaartenGr/BERTopic
3. ACL Anthology https://aclanthology.org/
4. SIGIR / ACM Digital Library https://dl.acm.org/