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InCaToMi: integrative causal topic miner between textual and non-textual time series data

Authors:

Carlos A. Ceja LimonAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2689 - 2691

https://doi.org/10.1145/2396761.2398727

Published: 29 October 2012 Publication History

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Abstract

Topic modeling is popular for text mining tasks. Recently, topic modeling has been combined with time lines when textual data is related to external non-textual time series data such as stock prices. However, no previous work has used the external non-textual time series data in the process of topic modeling. In this paper, we describe a novel text mining system, Integrative Causal Topic Miner (InCaToMi) that integrates textual and non-textual time series data. InCaToMi automatically finds causal relationships and topics using text data and external non-textual time series data using Granger Testing. Moreover, InCaToMi considers the non-textual time series data in the topic modeling process, using the time series data to iteratively improve modeling results through interactions between it and the textual data at both topic and word levels.

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Cited By

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Drury BGonçalo Oliveira Hde Andrade Lopes A(2022)A survey of the extraction and applications of causal relationsNatural Language Engineering10.1017/S135132492100036X28:3(361-400)Online publication date: 20-Jan-2022
https://doi.org/10.1017/S135132492100036X
Sharma RPalshikar G(2021)Virus Causes Flu: Identifying Causality in the Biomedical Domain Using an Ensemble Approach with Target-Specific Semantic EmbeddingsNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_9(93-104)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-3-030-80599-9_9
Sharma RPalshikar GPawar S(2018)An Unsupervised Approach for Cause-Effect Relation Extraction from Biomedical TextNatural Language Processing and Information Systems10.1007/978-3-319-91947-8_43(419-427)Online publication date: 22-May-2018
https://doi.org/10.1007/978-3-319-91947-8_43
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Index Terms

InCaToMi: integrative causal topic miner between textual and non-textual time series data
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications

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Information & Contributors

Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

View all

Drury BGonçalo Oliveira Hde Andrade Lopes A(2022)A survey of the extraction and applications of causal relationsNatural Language Engineering10.1017/S135132492100036X28:3(361-400)Online publication date: 20-Jan-2022
https://doi.org/10.1017/S135132492100036X
Sharma RPalshikar G(2021)Virus Causes Flu: Identifying Causality in the Biomedical Domain Using an Ensemble Approach with Target-Specific Semantic EmbeddingsNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_9(93-104)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-3-030-80599-9_9
Sharma RPalshikar GPawar S(2018)An Unsupervised Approach for Cause-Effect Relation Extraction from Biomedical TextNatural Language Processing and Information Systems10.1007/978-3-319-91947-8_43(419-427)Online publication date: 22-May-2018
https://doi.org/10.1007/978-3-319-91947-8_43
Kim HCastellanos MHsu MZhai CRietz TDiermeier DHe QIyengar ANejdl WPei JRastogi R(2013)Mining causal topics in text dataProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505612(885-890)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2505612
Kim HNikitin DZhai CCastellanos MHsu M(2013)Information Retrieval with Time Series QueryProceedings of the 2013 Conference on the Theory of Information Retrieval10.1145/2499178.2499195(56-63)Online publication date: 29-Sep-2013
https://dl.acm.org/doi/10.1145/2499178.2499195

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