Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset

• URL: http://arxiv.org/abs/2308.13545v1
• Date: Mon, 21 Aug 2023 22:18:09 GMT
• Title: Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset
• Authors: Md. Rafi-Ur-Rashid, Sami Azam, Mirjam Jonkman
• Abstract summary: Despite being the sixth most widely spoken language in the world, Bangla has received little attention due to the scarcity of text datasets. We collected, annotated, and prepared a comprehensive dataset of 212,184 Bangla documents in seven different categories and made it publicly accessible.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The selection of features for text classification is a fundamental task in text mining and information retrieval. Despite being the sixth most widely spoken language in the world, Bangla has received little attention due to the scarcity of text datasets. In this research, we collected, annotated, and prepared a comprehensive dataset of 212,184 Bangla documents in seven different categories and made it publicly accessible. We implemented three deep learning generative models: LSTM variational autoencoder (LSTM VAE), auxiliary classifier generative adversarial network (AC-GAN), and adversarial autoencoder (AAE) to extract text features, although their applications are initially found in the field of computer vision. We utilized our dataset to train these three models and used the feature space obtained in the document classification task. We evaluated the performance of the classifiers and found that the adversarial autoencoder model produced the best feature space.

Related papers

• Smart Expert System: Large Language Models as Text Classifiers [3.218954041700146]
  This paper introduces the Smart Expert System, a novel approach that leverages Large Language Models (LLMs) as text classifiers. The system simplifies the traditional text classification workflow, eliminating the need for extensive preprocessing and domain expertise. It is shown that the system's performance can be further enhanced through few-shot or fine-tuning strategies.
  arXiv  Detail & Related papers  (2024-05-17T04:05:05Z)
• Token Prediction as Implicit Classification to Identify LLM-Generated Text [37.89852204279844]
  This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation. Instead of adding an additional classification layer to a base LM, we reframe the classification task as a next-token prediction task. We utilize the Text-to-Text Transfer Transformer (T5) model as the backbone for our experiments.
  arXiv  Detail & Related papers  (2023-11-15T06:33:52Z)
• Leveraging Contextual Information for Effective Entity Salience Detection [21.30389576465761]
  We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. We also show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.
  arXiv  Detail & Related papers  (2023-09-14T19:04:40Z)
• Multi-Modal Classifiers for Open-Vocabulary Object Detection [104.77331131447541]
  The goal of this paper is open-vocabulary object detection (OVOD) We adopt a standard two-stage object detector architecture. We explore three ways via: language descriptions, image exemplars, or a combination of the two.
  arXiv  Detail & Related papers  (2023-06-08T18:31:56Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
  GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate. We build the first conversation-based information retrieval dataset employing AnnoLLM.
  arXiv  Detail & Related papers  (2023-03-29T17:03:21Z)
• Informative Text Generation from Knowledge Triples [56.939571343797304]
  We propose a novel memory augmented generator that employs a memory network to memorize the useful knowledge learned during the training. We derive a dataset from WebNLG for our new setting and conduct extensive experiments to investigate the effectiveness of our model.
  arXiv  Detail & Related papers  (2022-09-26T14:35:57Z)
• Modeling Multi-Granularity Hierarchical Features for Relation Extraction [26.852869800344813]
  We propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge.
  arXiv  Detail & Related papers  (2022-04-09T09:44:05Z)
• Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation [49.89831914386982]
  We propose a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. Our approach outperforms the pre-training of plain text using only 1/4 of the data.
  arXiv  Detail & Related papers  (2021-09-02T16:05:24Z)
• Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks [61.23408995934415]
  We propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%.
  arXiv  Detail & Related papers  (2021-02-23T04:14:34Z)
• GLEAKE: Global and Local Embedding Automatic Keyphrase Extraction [1.0681288493631977]
  We introduce Global and Local Embedding Automatic Keyphrase Extractor (GLEAKE) for the task of automatic keyphrase extraction. GLEAKE uses single and multi-word embedding techniques to explore the syntactic and semantic aspects of the candidate phrases. It refines the most significant phrases as a final set of keyphrases.
  arXiv  Detail & Related papers  (2020-05-19T20:24:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.