@inproceedings{shil-jin-2025-esaqueryrank,
    title = "{ESAQ}uery{R}ank: Ranking Query Interpretations for Document Retrieval Using Explicit Semantic Analysis",
    author = "Shil, Avijeet  and
      Jin, Wei",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.132/",
    pages = "1148--1152",
    abstract = "Representing query translation into relevant entities is a critical component of an infor- mation retrieval system. This paper proposes an unsupervised framework, ESAQueryRank, designed to process natural language queries by mapping n-gram phrases to Wikipedia ti- tles and ranking potential entity and phrase combinations using Explicit Semantic Analy- sis. Unlike previous approaches, this frame- work does not rely on query expansion, syn- tactic parsing, or manual annotation. Instead, it leverages Wikipedia metadata{---}such as ti- tles, redirects, disambiguation pages to dis- ambiguate entities and identify the most rel- evant ones based on cosine similarity in the ESA space. ESAQueryRank is evaluated using a random set of TREC questions and compared against a keyword-based approach and a context-based question translation model (CBQT). In all comparisons of full category types, ESAQueryRank consistently shows bet- ter results against both methods. Notably, the framework excels with more complex queries, achieving improvements in Mean Reciprocal Rank (MRR) of up to 480{\%} for intricate queries like those beginning with ``Why,'' even without explicitly incorporating the question type. These results demonstrate that ESA- QueryRank is an effective, transparent, and domain-independent framework for building natural language interfaces."
}