Difference between revisions of "Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model"

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== Overview ==
 
== Overview ==
 
In this research authors introduce a new approach for Arabic word sense disambiguation by utilizing [[Wikipedia]] as a lexical resource for disambiguation. The nearest sense for an ambiguous word is selected using Vector Space Model as a representation and cosine similarity between the word context and the retrieved senses from Wikipedia as a measure. Three experiments have been conducted to evaluate the proposed approach, two experiments use the first retrieved sentence for each sense from Wikipedia but they use different Vector Space Model representations while the third experiment uses the first paragraph for the retrieved sense from Wikipedia. The experiments show that using first paragraph is better than the first sentence and the use of TF-IDF is better than using abstract frequency in VSM. Also, the proposed approach is tested on English words and it gives better results using the first sentence retrieved from Wikipedia for each sense.
 
In this research authors introduce a new approach for Arabic word sense disambiguation by utilizing [[Wikipedia]] as a lexical resource for disambiguation. The nearest sense for an ambiguous word is selected using Vector Space Model as a representation and cosine similarity between the word context and the retrieved senses from Wikipedia as a measure. Three experiments have been conducted to evaluate the proposed approach, two experiments use the first retrieved sentence for each sense from Wikipedia but they use different Vector Space Model representations while the third experiment uses the first paragraph for the retrieved sense from Wikipedia. The experiments show that using first paragraph is better than the first sentence and the use of TF-IDF is better than using abstract frequency in VSM. Also, the proposed approach is tested on English words and it gives better results using the first sentence retrieved from Wikipedia for each sense.
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== Embed ==
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=== Wikipedia Quality ===
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Alian, Marwah; Awajan, Arafat; Alkouz, Akram. (2016). "[[Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model]]". Springer US. DOI: 10.1007/s10772-016-9376-y.
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=== English Wikipedia ===
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{{cite journal |last1=Alian |first1=Marwah |last2=Awajan |first2=Arafat |last3=Alkouz |first3=Akram |title=Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model |date=2016 |doi=10.1007/s10772-016-9376-y |url=https://wikipediaquality.com/wiki/Word_Sense_Disambiguation_for_Arabic_Text_Using_Wikipedia_and_Vector_Space_Model |journal=Springer US}}
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=== HTML ===
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Alian, Marwah; Awajan, Arafat; Alkouz, Akram. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Word_Sense_Disambiguation_for_Arabic_Text_Using_Wikipedia_and_Vector_Space_Model">Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model</a>&amp;quot;. Springer US. DOI: 10.1007/s10772-016-9376-y.
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Revision as of 06:44, 5 May 2020


Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model
Authors
Marwah Alian
Arafat Awajan
Akram Alkouz
Publication date
2016
DOI
10.1007/s10772-016-9376-y
Links
Original

Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model - scientific work related to Wikipedia quality published in 2016, written by Marwah Alian, Arafat Awajan and Akram Alkouz.

Overview

In this research authors introduce a new approach for Arabic word sense disambiguation by utilizing Wikipedia as a lexical resource for disambiguation. The nearest sense for an ambiguous word is selected using Vector Space Model as a representation and cosine similarity between the word context and the retrieved senses from Wikipedia as a measure. Three experiments have been conducted to evaluate the proposed approach, two experiments use the first retrieved sentence for each sense from Wikipedia but they use different Vector Space Model representations while the third experiment uses the first paragraph for the retrieved sense from Wikipedia. The experiments show that using first paragraph is better than the first sentence and the use of TF-IDF is better than using abstract frequency in VSM. Also, the proposed approach is tested on English words and it gives better results using the first sentence retrieved from Wikipedia for each sense.

Embed

Wikipedia Quality

Alian, Marwah; Awajan, Arafat; Alkouz, Akram. (2016). "[[Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model]]". Springer US. DOI: 10.1007/s10772-016-9376-y.

English Wikipedia

{{cite journal |last1=Alian |first1=Marwah |last2=Awajan |first2=Arafat |last3=Alkouz |first3=Akram |title=Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model |date=2016 |doi=10.1007/s10772-016-9376-y |url=https://wikipediaquality.com/wiki/Word_Sense_Disambiguation_for_Arabic_Text_Using_Wikipedia_and_Vector_Space_Model |journal=Springer US}}

HTML

Alian, Marwah; Awajan, Arafat; Alkouz, Akram. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Word_Sense_Disambiguation_for_Arabic_Text_Using_Wikipedia_and_Vector_Space_Model">Word Sense Disambiguation for Arabic Text Using Wikipedia and Vector Space Model</a>&quot;. Springer US. DOI: 10.1007/s10772-016-9376-y.