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Prediction Of Short Text In Weblogs For Accurate Classification Using Iohe A Machine Learning Technique.

Grouping small texts to one learning category or clusters interpreting that brings similar texts is weblogs or Chat rooms more complex and the necessary for suggestions in various fields such as medical or education are mandatory. We can achieve this semantic mapping and categorization by applying a...

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Published in:Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1320 - 1324
Main Authors: DEEPA, N., T., DEVI
Format: research tables/charts Journal Article
Published: Turkish Journal of Physiotherapy & Rehabilitation 2021
Online Access:View this record in EBSCOhost
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        atl: Prediction Of Short Text In Weblogs For Accurate Classification Using Iohe A Machine Learning Technique.
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          DEEPA, N.
          T., DEVI
        affil: Assistant Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India
      sug:
        subj:
          Blogs
          Machine Learning
          Learning Methods
          Human
          Semantics
          Grammar
          Natural Language Processing
          Coding
          Descriptive Statistics
      ab: Grouping small texts to one learning category or clusters interpreting that brings similar texts is weblogs or Chat rooms more complex and the necessary for suggestions in various fields such as medical or education are mandatory. We can achieve this semantic mapping and categorization by applying a machine learning technique which produces easy understanding improved one hot encoding (IOHE) as vector representation of small text. Also can be allotted the similar classifying group to messages that are alike binary representations. When there is activity or service context in small text we arises a proposed system Machine Learning (ML) technique for semantic binary codes by grouping verbs, noun from the small message and annotate the mutual understanding content, adding the associated texts classification with the support of introduced terms. Alternatively verbs are also utilizing the technique by trimming the indistinct context that is available in the short text. The proposed model using Machine learning that manages the text to be processed easily and avoid huge time for processing the same information or unwanted context matching while labels are being predicted. So the group or category of label can improve the supervised learning and refer the verbs and noun in accurate manner.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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