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Exploration of Word Embeddings With Graph-Based Context Adaptation for Enhanced Word Vectors.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Exploration of Word Embeddings With Graph-Based Context Adaptation for Enhanced Word Vectors./
作者:	Sandhu, Tanvi.
面頁冊數:	1 online resource (73 pages)
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798382903248

Exploration of Word Embeddings With Graph-Based Context Adaptation for Enhanced Word Vectors.
Sandhu, Tanvi.

Exploration of Word Embeddings With Graph-Based Context Adaptation for Enhanced Word Vectors. - 1 online resource (73 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.Sc.)--University of Windsor (Canada), 2024.

Includes bibliographical references

In the aspect of information storage, text assumes a central role, necessitating streamlined and effective methods for swift retrieval. Among various text representations, the vector form stands out for its remarkable efficiency, especially when dealing with large datasets. Arranging words that are similar in meaning close to each other in the vectorized representation helps improve system performance in different Natural Language Processing (NLP) tasks. Previous methods, primarily centered on capturing word context through neural language models, have fallen short in delivering high scores for word similarity problems. This thesis investigates the connection between representing words in vector form and the improved performance and accuracy observed in NLP tasks. It introduces a method to represent words as a graph so that their first-order and second-order proximity are preserved, aiming to enhance overall capabilities in semantic representation. Experimental deployment of this technique across diverse text corpora underscores its superiority over conventional word embedding approaches. This method of word representation outperforms traditional word-embedding methods by 2.7% in multiple intrinsic and extrinsic tasks. The findings contribute to the evolving landscape of semantic representation learning but also illuminate their implications for text classification tasks, especially within the context of dynamic embedding models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382903248Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

GraphIndex Terms--Genre/Form:

554714
Electronic books.

Exploration of Word Embeddings With Graph-Based Context Adaptation for Enhanced Word Vectors.
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520 $a In the aspect of information storage, text assumes a central role, necessitating streamlined and effective methods for swift retrieval. Among various text representations, the vector form stands out for its remarkable efficiency, especially when dealing with large datasets. Arranging words that are similar in meaning close to each other in the vectorized representation helps improve system performance in different Natural Language Processing (NLP) tasks. Previous methods, primarily centered on capturing word context through neural language models, have fallen short in delivering high scores for word similarity problems. This thesis investigates the connection between representing words in vector form and the improved performance and accuracy observed in NLP tasks. It introduces a method to represent words as a graph so that their first-order and second-order proximity are preserved, aiming to enhance overall capabilities in semantic representation. Experimental deployment of this technique across diverse text corpora underscores its superiority over conventional word embedding approaches. This method of word representation outperforms traditional word-embedding methods by 2.7% in multiple intrinsic and extrinsic tasks. The findings contribute to the evolving landscape of semantic representation learning but also illuminate their implications for text classification tasks, especially within the context of dynamic embedding models.
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538 $a Mode of access: World Wide Web
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