@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://aclanthology.org/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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shil-jin-2025-esaqueryrank">
<titleInfo>
<title>ESAQueryRank: Ranking Query Interpretations for Document Retrieval Using Explicit Semantic Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Avijeet</namePart>
<namePart type="family">Shil</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Jin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
</titleInfo>
<name type="personal">
<namePart type="given">Galia</namePart>
<namePart type="family">Angelova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Kunilovskaya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marie</namePart>
<namePart type="family">Escribe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">shil-jin-2025-esaqueryrank</identifier>
<location>
<url>https://aclanthology.org/2025.ranlp-1.132/</url>
</location>
<part>
<date>2025-09</date>
<extent unit="page">
<start>1148</start>
<end>1152</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ESAQueryRank: Ranking Query Interpretations for Document Retrieval Using Explicit Semantic Analysis
%A Shil, Avijeet
%A Jin, Wei
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F shil-jin-2025-esaqueryrank
%X 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.
%U https://aclanthology.org/2025.ranlp-1.132/
%P 1148-1152
Markdown (Informal)
[ESAQueryRank: Ranking Query Interpretations for Document Retrieval Using Explicit Semantic Analysis](https://aclanthology.org/2025.ranlp-1.132/) (Shil & Jin, RANLP 2025)
ACL