Precision of machine learning models with word embeddings across varying window sizes, highlighting performance patterns.

Precision of machine learning models with word embeddings across varying window sizes, highlighting performance patterns.

<p>Precision of machine learning models with word embeddings across varying window sizes, highlighting performance patterns.</p>

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Bibliographic Details
Main Author:	Arshad Iqbal (375576) (author)
Other Authors:	Abdul Shahid (13364811) (author), Muhammad Roman (11370937) (author), Muhammad Tanvir Afzal (4162504) (author), Umair ul Hassan (20928222) (author)
Published:	2025
Subjects:	Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines garnered significant attention dimensional distributed representations achieving superior results optimising window size scientific literature act larger citation windows appropriate word embedding various window sizes fixed window sizes xlink "> citations intent </ p evaluation employs precision convolutional neural networks deep learning models including text classification text citations based machine learning models window sizes deep learning classification models word2vec embedding scientific knowledge citation analysis text citations classification model classification approaches citations hold analyze citations worth noting term memory substantial volume sentiment analysis often overlook numerous taxonomies nuanced meanings naive bayes found application findings reveal current state contextual meaning comparative analysis accuracy metrics
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Summary:	<p>Precision of machine learning models with word embeddings across varying window sizes, highlighting performance patterns.</p>

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