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Active High-Recall Information Retrieval from Domain-Specific Text Corpora based on Query Documents

Authors:

Abidalrahman Moh'd,

Seyednaser Nourashrafeddin,

Evangelos MiliosAuthors Info & Claims

DocEng '18: Proceedings of the ACM Symposium on Document Engineering 2018

Article No.: 12, Pages 1 - 10

https://doi.org/10.1145/3209280.3209532

Published: 28 August 2018 Publication History

Abstract

In this paper, we propose a high recall active document retrieval system for a class of applications involving query documents, as opposed to key terms, and domain-specific document corpora. The output of the model is a list of documents retrieved based on the domain expert feedback collected during training. A modified version of Bag of Word (BoW) representation and a semantic ranking module, based on Google n-grams, are used in the model. The core of the system is a binary document classification model which is trained through a continuous active learning strategy. In general, finding or constructing training data for this type of problem is very difficult due to either confidentiality of the data, or the need for domain expert time to label data. Our experimental results on the retrieval of Call For Papers based on a manuscript demonstrate the efficacy of the system to address this application and its performance compared to other candidate models.

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Cited By

Lechtenberg FFarreres JGalvan-Cara ASomoza-Tornos AEspuña AGraells M(2022)Information retrieval from scientific abstract and citation databasesExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116967199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116967
Baggio CCecchini RMaguitman AMilios EBorghoff USchimmler S(2019)Multi-Objective GP Strategies for Topical Search Integrating Wikipedia ConceptsProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345402(1-10)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3342558.3345402

Index Terms

Active High-Recall Information Retrieval from Domain-Specific Text Corpora based on Query Documents
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

DocEng '18: Proceedings of the ACM Symposium on Document Engineering 2018

August 2018

311 pages

ISBN:9781450357692

DOI:10.1145/3209280

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGDOC: ACM Special Interest Group on Systems Documentation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 August 2018

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Natural Sciences and Engineering Research Council of Canada

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DocEng '18

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SIGWEB

DocEng '18: ACM Symposium on Document Engineering 2018

August 28 - 31, 2018

NS, Halifax, Canada

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Overall Acceptance Rate 194 of 564 submissions, 34%

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Cited By

Lechtenberg FFarreres JGalvan-Cara ASomoza-Tornos AEspuña AGraells M(2022)Information retrieval from scientific abstract and citation databasesExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116967199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116967
Baggio CCecchini RMaguitman AMilios EBorghoff USchimmler S(2019)Multi-Objective GP Strategies for Topical Search Integrating Wikipedia ConceptsProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345402(1-10)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3342558.3345402

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