Leveraging the meta-embedding for text classification in a resource-constrained language

Hossain, Md. Rajib; Hoque, Mohammed Moshiul; Siddique, Nazmul

doi:10.1016/j.engappai.2023.106586

Cited by 16 publications

(2 citation statements)

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“…Text classification, especially sentiment analysis, has become one of the very important NLP applications. It helps in processing millions of entries to deduct a sense of a mass satisfaction / dissatisfaction through users' produced text which helps companies and/or organizations and/or scientists to form perspective and/or decisions which are based on a big picture and developing applications that serves real-life solutions as discussed in [16,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…That approach might sound intuitive but at the same time it creates a new set of greater challenges. The first one is the need to provide and maintain a specific model and lexicons for every single language and its dialects, which is already a huge challenge in NLP domain due the variant complexity of many languages and dialects regarding grammar and structure to begin with as these studies show [11,[21][22][23]25]. Then comes the challenge of the doubled resources needed, due to having two ML models (at least) running instead of one.…”

Section: Introductionmentioning

confidence: 99%

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Effect of typos on text classification accuracy in word and character tokenization

Peter E. Shawky,

Saleh Mesbah ElKaffas,

Shawkat K Guirguis

2024

ARASET

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To train a machine to “sense” a users’ feelings through writings (sentiment analysis) has become a crucial process in several domains: marketing, research, surveys and more. Nevertheless in times of crisis like COVID. Typo is one of the underestimated challenges processing user-generated text (comments, tweets, ..etc), it affects both learning and evaluation processes. Word tokenization outcome changes drastically even with a single character change, hence as expected, experiments have shown significant accuracy decreases due to typo. Adding a spelling correction as preprocessing layer, building one for every language, is a very time and resources expensive solution, a huge challenge against large data and real-time processing. Alternatively, a CNN model consuming the same text, once tokenized on characters level and once on words level while inducing typo, showed that as the typo percentage approaches 10% of the text, the results with characters tokens surpasses words tokens. Finally, on %30 typo of the text, the model consuming characters tokenization outperformed itself with the word level by a significant %22.3 in accuracy and %24.9 in F1-Score, using the same exact model. This approach in solving the inevitable typo challenge in NLP proved to be of significant practicality, saving huge resources versus using a spelling-correction model beforehand. It also removes a blocker challenge in front of real-time processing of user-generated text while preserving acceptable accuracy results.

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Section: Introductionmentioning

confidence: 99%