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AnaSearch: Extract, Retrieve and Visualize Structured Results from Unstructured Text for Analytical Queries

Authors:

Dongmei ZhangAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Pages 906 - 909

https://doi.org/10.1145/3437963.3441694

Published: 08 March 2021 Publication History

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Abstract

Modern search engines retrieve results mainly based on the keyword matching techniques, and thus fail to answer analytical queries like "apps with more than 1 billion monthly active users" or "population growth of the US from 2015 to 2019", which requires numerical reasoning or aggregating results from multiple web pages. Such analytical queries are very common in the data analysis area, the expected results would be structured tables or charts. In most cases, these structured results are not available or accessible, they scatter in various text sources. In this work, we build AnaSearch, a search system to support analytical queries, and return structured results that can be visualized in the form of tables or charts. We collect and build structured quantitative data from the unstructured text on the web automatically. With AnaSearch, data analysts could easily derive insights for decision making with keyword or natural language queries. Specifically, we build AnaSearch under the COVID-19 news data, which makes it easy to compare with manually collected structured data.

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Cited By

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Liu HDas ABoltz AZhou DPinaroc DLease MLee M(2024)Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIProceedings of the ACM on Human-Computer Interaction10.1145/36869628:CSCW2(1-44)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686962
Almasian SKosnac AGertz M(2024)QuantPlorer: Exploration of Quantities in TextAdvances in Information Retrieval10.1007/978-3-031-56069-9_13(171-176)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_13
Rybinski MWan SKarimi SParis CJin BHuth NThorburn PHolzworth DChen HDuh WHuang HKato MMothe JPoblete B(2023)SciHarvester: Searching Scientific Documents for Numerical ValuesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591808(3135-3139)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591808
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Index Terms

AnaSearch: Extract, Retrieve and Visualize Structured Results from Unstructured Text for Analytical Queries
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
    2. Specialized information retrieval
      1. Structure and multilingual text search
        Structured text search

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Information & Contributors

Information

Published In

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 March 2021

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WSDM '21

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WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

Virtual Event, Israel

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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Liu HDas ABoltz AZhou DPinaroc DLease MLee M(2024)Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIProceedings of the ACM on Human-Computer Interaction10.1145/36869628:CSCW2(1-44)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686962
Almasian SKosnac AGertz M(2024)QuantPlorer: Exploration of Quantities in TextAdvances in Information Retrieval10.1007/978-3-031-56069-9_13(171-176)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_13
Rybinski MWan SKarimi SParis CJin BHuth NThorburn PHolzworth DChen HDuh WHuang HKato MMothe JPoblete B(2023)SciHarvester: Searching Scientific Documents for Numerical ValuesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591808(3135-3139)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591808
Das ALiu HKovatchev VLease M(2023)The state of human-centered NLP technology for fact-checkingInformation Processing & Management10.1016/j.ipm.2022.10321960:2(103219)Online publication date: Mar-2023
https://doi.org/10.1016/j.ipm.2022.103219
Chai LXiao DYan ZYang JYang LZhang QCao YLi Z(2023)QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic ParsingPRICAI 2023: Trends in Artificial Intelligence10.1007/978-981-99-7022-3_24(275-286)Online publication date: 10-Nov-2023
https://doi.org/10.1007/978-981-99-7022-3_24
Almasian SBruseva MGertz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)QFinderProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531672(3272-3277)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531672

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