Table 2 Summary of Methodology and Current deep learning (DL) and machine learning (ML) based part of speech tagging studies
From: Part of speech tagging: a systematic review of deep learning and machine learning approaches
Study | Algorithms | Methodology | |
|---|---|---|---|
ML | DL | ||
Kumar et al. [69] | √ | Recurrent neural network, long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM | |
Mohammed [51] | √ | Hidden markov model and CRF with neural network model | |
Besharati et al. [54] | √ | Single-layer and a two-layer MLP and LSTM neural network | |
Hirpssa and Lehal [39] | √ | Conditional Random Field, HMM-based, and Naive Bays | |
Anastasyev et al. [59] | √ | Feedforward Neural Network | |
Mishra [66] | √ | CRF, SVM, structured perceptron and neural network | |
Gashaw and Shashirekha [46] | √ | Machine learning algorithms (Brill, TnT, and CRFSuit Taggers) | |
Khan et al. [33] | √ | √ | CRF, support vector machine (SVM), deep recurrent neural network (DRNN), and n-gram Markov model the bigram hidden Markov model (HMM) |
Singh et al. [56] | √ | LSTM with RNN | |
Baig et al. [70] | √ | √ | TnT tagger, averaged perceptron, cyclic dependency network and guided learning framework for bidirectional sequence classification |
Bonchanoski and Zdravkova [71] | √ | Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory (BiLSTM) | |
Sarbin et al. [61] | √ | Sequential deep learning methods (Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM) | |
Kumar et al. [72] | √ | Deep Neural Network | |
Alharbi et al. [1] | √ | √ | SVM and Bi-LSTM |
Kabir et al. [73] | √ | Convolutional Neural Network | |
Meftah et al. [65] | √ | Long Short-Term Memory (LSTM) recurrent neural networks and their bidirectional versions (Bi-LSTM RNNs) | |
Deshmukh and Kiwelekar [14] | √ | Bidirectional long short-term memory (Bi-LSTM) and deep learning | |
Argaw [55] | √ | Recurrent Neural Network (RNN), Long Short-term Memory Networks (LSTM), Gated Recurrent Unit (GRU) | |
Prabha et al. [60] | √ | Bidirectional LSTM and auto encoder | |
Srivastava et al. [63] | √ | Deep Learning (BiLSTM autoencoder) | |
Attia et al. [62] | √ | Hidden Markov Model | |
Patoary et al. [74] | √ | Deep Learning | |
Gopalakrishnan et al. [58] | √ | Deep learning (RNN, LSTM, and GRU) | |
Bahcevan et al. [57] | √ | Long Short-term Memory (LSTM) and Recurrent Neural Network (RNN) | |
Akhil et al. [75] | √ | Deep neural network (RNN, GRU, LSTM and BLSTM) | |