Deep Learning for Hindi Text Classification: A Comparison

Joshi, Ramchandra; Goel, Purvi; Joshi, Raviraj

doi:10.1007/978-3-030-44689-5_9

Cited by 39 publications

(25 citation statements)

References 12 publications

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“…In this work, we provide a comparative view of different families of algorithms on a range of datasets. Similar comparison of deep learning approaches on different datasets and languages have been studied in [14,13,9,32,10,17].…”

Section: Introductionmentioning

confidence: 88%

Comparative Study of Long Document Classification

Wagh,

Khandve,

Joshi

et al. 2021

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The amount of information stored in the form of documents on the internet has been increasing rapidly. Thus it has become a necessity to organize and maintain these documents in an optimum manner. Text classification algorithms study the complex relationships between words in a text and try to interpret the semantics of the document. These algorithms have evolved significantly in the past few years. There has been a lot of progress from simple machine learning algorithms to transformer-based architectures. However, existing literature has analyzed different approaches on different data sets thus making it difficult to compare the performance of machine learning algorithms. In this work, we revisit long document classification using standard machine learning approaches. We benchmark approaches ranging from simple Naive Bayes to complex BERT on six standard text classification datasets. We present an exhaustive comparison of different algorithms on a range of long document datasets. We re-iterate that long document classification is a simpler task and even basic algorithms perform competitively with BERT-based approaches on most of the datasets. The BERT-based models perform consistently well on all the datasets and can be blindly used for the document classification task when the computations cost is not a concern. In the shallow model's category, we suggest the usage of raw BiLSTM + Max architecture which performs decently across all the datasets. Even simpler Glove + Attention bag of words model can be utilized for simpler use cases. The importance of using sophisticated models is clearly visible in the IMDB sentiment dataset which is a comparatively harder task.

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Section: Introductionmentioning

confidence: 88%

Comparative Study of Long Document Classification

Wagh,

Khandve,

Joshi

et al. 2021

Preprint

Self Cite

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“…Various Hindi text classification approaches have been studied in [8] using BOW, CNN, LSTM, BiLSTM, BERT, and LASER models. The work is particularly focused on Hindi text classification.…”

Section: Related Workmentioning

confidence: 99%

“…Hindi is one of the official languages of India and is spoken by around 45% of its population [8]. Due to its popularity in India, there are a large number of social media activities performed in the Hindi language written in Devanagari script.…”

Section: Introductionmentioning

confidence: 99%

Hate and Offensive Speech Detection in Hindi and Marathi

Velankar¹,

Patil²,

Gore³

et al. 2021

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Sentiment analysis is the most basic NLP task to determine the polarity of text data. There has been a significant amount of work in the area of multilingual text as well. Still hate and offensive speech detection faces a challenge due to inadequate availability of data, especially for Indian languages like Hindi and Marathi. In this work, we consider hate and offensive speech detection in Hindi and Marathi texts. The problem is formulated as a text classification task using the state of the art deep learning approaches. We explore different deep learning architectures like CNN, LSTM, and variations of BERT like multilingual BERT, IndicBERT, and monolingual RoBERTa. The basic models based on CNN and LSTM are augmented with fast text word embeddings. We use the HASOC 2021 Hindi and Marathi hate speech datasets to compare these algorithms. The Marathi dataset consists of binary labels and the Hindi dataset consists of binary as well as more-fine grained labels. We show that the transformer-based models perform the best and even the basic models along with FastText embeddings give a competitive performance. Moreover, with normal hyper-parameter tuning, the basic models perform better than BERT-based models on the fine-grained Hindi dataset.

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“…Deep learning models have been used widely to tackle text classification as they tend to perform best compared to other machine learning techniques [4] [5]. Some of the most popular methods used for this task are based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [6][7] [8]. Pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) based on transformer architecture, perform better than neural networks trained from scratch [9].…”

Section: Introductionmentioning

confidence: 99%

On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations

Miyajiwala,

Ladkat,

Jagadale

et al. 2022

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Text classification is a fundamental Natural Language Processing task that has a wide variety of applications, where deep learning approaches have produced state-of-the-art results. While these models have been heavily criticized for their black-box nature, their robustness to slight perturbations in input text has been a matter of concern. In this work, we carry out a data-focused study evaluating the impact of systematic practical perturbations on the performance of the deep learning based text classification models like CNN, LSTM, and BERT-based algorithms. The perturbations are induced by the addition and removal of unwanted tokens like punctuation and stop-words that are minimally associated with the final performance of the model. We show that these deep learning approaches including BERT are sensitive to such legitimate input perturbations on four standard benchmark datasets SST2, TREC-6, BBC News, and tweet eval. We observe that BERT is more susceptible to the removal of tokens as compared to the addition of tokens. Moreover, LSTM is slightly more sensitive to input perturbations as compared to CNN based model. The work also serves as a practical guide to assessing the impact of discrepancies in train-test conditions on the final performance of models.

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Deep Learning for Hindi Text Classification: A Comparison

Cited by 39 publications

References 12 publications

Comparative Study of Long Document Classification

Comparative Study of Long Document Classification

Hate and Offensive Speech Detection in Hindi and Marathi

On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations

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