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Semantic text classification of disease reporting

Published: 23 July 2007 Publication History

Abstract

Traditional text classification studied in the IR literature is mainly based on topics. That is, each class or category represents a particular topic, e.g., sports, politics or sciences. However, many real-world text classification problems require more refined classification based on some semantic aspects. For example, in a set of documents about a particular disease, some documents may report the outbreak of the disease, some may describe how to cure the disease, some may discuss how to prevent the disease, and yet some others may include all the above information. To classify text at this semantic level, the traditional "bag of words" model is no longer sufficient. In this paper, we report a text classification study at the semantic level and show that sentence semantic and structure features are very useful for such kind of classification. Our experimental results based on a disease outbreak dataset demonstrated the effectiveness of the proposed approach.

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Cited By

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  • (2021)A Smart Web Application for Symptom‐Based Disease Detection and Prediction Using State‐of‐the‐Art ML and ANN ModelsComputational Intelligence and Healthcare Informatics10.1002/9781119818717.ch4(65-80)Online publication date: 25-Oct-2021
  • (2017)Mechanism for Structuring the Data from a Generic Identity Document Image using Semantic AnalysisProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3131594(213-216)Online publication date: 17-Oct-2017
  • (2017)Learning to recommend descriptive tags for health seekers using deep learning2017 International Conference on Inventive Systems and Control (ICISC)10.1109/ICISC.2017.8068589(1-7)Online publication date: Jan-2017
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
July 2007
946 pages
ISBN:9781595935977
DOI:10.1145/1277741
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2007

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Author Tags

  1. semantics
  2. text classfication

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SIGIR07
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SIGIR07: The 30th Annual International SIGIR Conference
July 23 - 27, 2007
Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2021)A Smart Web Application for Symptom‐Based Disease Detection and Prediction Using State‐of‐the‐Art ML and ANN ModelsComputational Intelligence and Healthcare Informatics10.1002/9781119818717.ch4(65-80)Online publication date: 25-Oct-2021
  • (2017)Mechanism for Structuring the Data from a Generic Identity Document Image using Semantic AnalysisProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3131594(213-216)Online publication date: 17-Oct-2017
  • (2017)Learning to recommend descriptive tags for health seekers using deep learning2017 International Conference on Inventive Systems and Control (ICISC)10.1109/ICISC.2017.8068589(1-7)Online publication date: Jan-2017
  • (2015)Disease Inference from Health-Related Questions via Sparse Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.239929827:8(2107-2119)Online publication date: 1-Aug-2015

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