Adaptative NLP for Low Resource Environment and SAFE: Semantic-Syntactic Based Text Augmentation Technique

Simple item page
dc.contributor.advisorShahriar Karim
dc.contributor.authorSunjare Zulfiker
dc.contributor.authorS.M. Ashraful Hasan
dc.contributor.authorFahid Shadman Karim
dc.contributor.id1912050042
dc.contributor.id1911570042
dc.contributor.id1911110042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-19
dc.date.accessioned2024-05-19T06:29:09Z
dc.date.available2024-05-19T06:29:09Z
dc.date.issued2023
dc.description.abstractNatural language processing (NLP) is a popular method for extracting context and syntax from text data for use in automation. NLP research focuses on dominant languages and advanced computing, neglecting low-resourced languages with limited computational capabilities. In this research, We propose the use of data augmentation (SAFE) approaches, suggest methods including Fusion architecture models (BiLSTM+CNN+BiLSTM) with optimized cyclic learning rate scheduler, layer-wise optimization, freezing early layers, and employ the multichannel approach to address the issue of low resource text data. In the domain of NLP where vast amounts of labeled data are scarce or expensive to collect, we offer our proposed method of SAFE augmentation technique for sentiment classification. This augmentation technique identifies the Subject, Verb, and Object in a sentence and alternates their position in six different permutations. The Object is also broken down into Subject, Verb, and Object again and is further rearranged. This aims to preserve the semantic meaning of the sentence while allowing some syntactic variation. We have also generated and calculated semantic and syntactic scores of the augmented sentences with respect to the original sentences.
dc.description.degreeUndergraduate
dc.identifier.cd600000342
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/757
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSUlibrary
dc.titleAdaptative NLP for Low Resource Environment and SAFE: Semantic-Syntactic Based Text Augmentation Technique
dc.typeProject
oaire.citation.endPage49
oaire.citation.startPage1
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
600000342-Abstract.pdf
Size:
138.94 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
600000342.pdf
Size:
1.46 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.93 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Theses - Undergraduate