- paper 01
In this paper, they have discussed Bangla text generation, using a deep learning approach, LSTM, predicts the very next point of a given time sequence.
- paper 02
In this paper, they fine-tune multilingual transformer models for Bangla Text Classification tasks in different domains, including sentiment analysis, emotion detection, news categorization, and authorship attribution
- paper 03
A systematic review of natural language processing for classification tasks in T the field of incident reporting and adverse event analysis
In this paper they classify incident reports and adverse events in healthcare
- paper 04
In this paper, they introduce a hybrid feature extracted and selected by coupling a weighting scheme term frequency-inverse document frequency-inverse class frequency (tf-idf-icf) and mod- ified chi-square from the text documents
- paper 05
In this paper, they find a way to reduce the dimension of the feature vector by using Turkish’s grammar rules without compromising success rates is explained. The feature vector is weighted on the basis of the word frequency as the word stems have been selected as features.
- paper 06
This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the AWD-LSTM architecture and various other techniques suitable for training in Bangla language.
- paper 07
In this paper, they are trying to implement the tri-gram, 4-gram and 5-gram model of word clustering for Bangla to observe which one is the best among them with quite a large corpus of approximate 1 lakh Bangla words.
- paper 08
In this paper, It calculates correlations of the assessments collected through a survey among different individuals. A conceptual distance is used to suggest the implicit pragmatic nature of the Bangla words and it also implies an elementary taxonomy for Bangla words.