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MY201295A - A computer-implemented method for self-learning text relevance and determining text relevancy - Google Patents

A computer-implemented method for self-learning text relevance and determining text relevancy

Info

Publication number
MY201295A
MY201295A MYPI2017705112A MYPI2017705112A MY201295A MY 201295 A MY201295 A MY 201295A MY PI2017705112 A MYPI2017705112 A MY PI2017705112A MY PI2017705112 A MYPI2017705112 A MY PI2017705112A MY 201295 A MY201295 A MY 201295A
Authority
MY
Malaysia
Prior art keywords
self
data structures
content
learning
user data
Prior art date
Application number
MYPI2017705112A
Inventor
Anand Sadanandan Arun
Saifuddin Hussain
Khairuddin Ahamad Muhammad
Hong Hoe Ong
Original Assignee
Mimos Berhad
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mimos Berhad filed Critical Mimos Berhad
Priority to MYPI2017705112A priority Critical patent/MY201295A/en
Priority to PCT/MY2018/050084 priority patent/WO2019132647A1/en
Publication of MY201295A publication Critical patent/MY201295A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Machine Translation (AREA)

Abstract

The present invention discloses a computer-implemented method of self-learning text relevance for determining relevancy of a text (100), comprises the steps of receiving user defined phrases via user interface; creating a user data model, by a processor incorporated with a machine learning algorithm, from the user defined phrases (120) using a domain knowledge, wherein the user data model comprising user data structures; extracting a content of document by the processor; creating content data structures (140) from the extracted content of document using the domain knowledge, generating a self-learning model (160) from said content data structures and the user data model; determining semantic distance between said content data structures and said user data structures; and characterized by updating the self-learning model is updated (180) from said content data structures in reference to the user data model if the semantic distance is greater than a predefined threshold value. The present invention also relates to a computer-implemented method to determine relevancy of a document, which incorporates the self-learning text relevance for semantic content analysis and updates.
MYPI2017705112A 2017-12-28 2017-12-28 A computer-implemented method for self-learning text relevance and determining text relevancy MY201295A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
MYPI2017705112A MY201295A (en) 2017-12-28 2017-12-28 A computer-implemented method for self-learning text relevance and determining text relevancy
PCT/MY2018/050084 WO2019132647A1 (en) 2017-12-28 2018-11-30 A method for self-learning text relevance and determining text relevancy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
MYPI2017705112A MY201295A (en) 2017-12-28 2017-12-28 A computer-implemented method for self-learning text relevance and determining text relevancy

Publications (1)

Publication Number Publication Date
MY201295A true MY201295A (en) 2024-02-15

Family

ID=65818577

Family Applications (1)

Application Number Title Priority Date Filing Date
MYPI2017705112A MY201295A (en) 2017-12-28 2017-12-28 A computer-implemented method for self-learning text relevance and determining text relevancy

Country Status (2)

Country Link
MY (1) MY201295A (en)
WO (1) WO2019132647A1 (en)

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6076051A (en) 1997-03-07 2000-06-13 Microsoft Corporation Information retrieval utilizing semantic representation of text
US7716216B1 (en) 2004-03-31 2010-05-11 Google Inc. Document ranking based on semantic distance between terms in a document

Also Published As

Publication number Publication date
WO2019132647A1 (en) 2019-07-04

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