A hybrid statistical and deep learning based technique for Persian part of speech tagging

Besharati, Sara; Veisi, Hadi; Darzi, Ali; Saravani, Seyed Habib Hosseini

doi:10.1007/s42044-020-00063-1

Cited by 12 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, LSTM and Bi-LSTM networks have been widely used in natural language processing tasks, such as parts-of-speech tagging [24,25], Chinese word segmentation [26,27] and named entity recognition [28], and they have achieved outstanding results. In order to improve the efficiency of morpheme segmentation and stem extraction processes for Uyghur-Kazakh-Kirghiz, this study drew on the idea of Chinese word segmentation and transformed the task of morpheme segmentation into the label classification problem of morpheme sequences, and built a Uyghur-Kazakh-Kirghiz morpheme segmentation model based on the Bi-LSTM and CRF networks.…”

Section: Morpheme Segmentation Methods Based On the Bi-lstm_crfmentioning

confidence: 99%

Uyghur–Kazakh–Kirghiz Text Keyword Extraction Based on Morpheme Segmentation

et al. 2023

View full text Add to dashboard Cite

In this study, based on a morpheme segmentation framework, we researched a text keyword extraction method for Uyghur, Kazakh and Kirghiz languages, which have similar grammatical and lexical structures. In these languages, affixes and a stem are joined together to form a word. A stem is a word particle with a notional meaning, while the affixes perform grammatical functions. Because of these derivative properties, the vocabularies used for these languages are huge. Therefore, pre-processing is a necessary step in NLP tasks for Uyghur, Kazakh and Kirghiz. Morpheme segmentation enabled us to remove the suffixes as the auxiliary unit while retaining the meaningful stem and it reduced the dimension of the feature space present in the keyword extraction task for Uyghur, Kazakh and Kirghiz texts. We transformed the morpheme segmentation task into the problem of labeling the morpheme sequences, and we used the Bi-LSTM network to bidirectionally obtain the position feature information of character sequences. We applied CRF to effectively learn the information of the preceding and following label sequences to build a highly accurate Bi-LSTM_CRF morpheme segmentation model, and we prepared morpheme-based experimental text sets by using this model. Subsequently, we used the stem vectors’ similarity to modify the TextRank algorithm, subsequent to the training of the stem embedding vector using the Doc2vec algorithm, and then we performed a text keyword extraction experiment. In this experiment, the highest F1 scores of 43.8%, 44% and 43.9% were obtained for three datasets. The experimental results show that the morpheme-based approach provides much better results than the word-based approach, which shows the stem vector similarity weighting is an efficient method for the text keyword extraction task, thus proving the efficiency of morpheme sequence for morphologically derivative languages.

show abstract

Section: Morpheme Segmentation Methods Based On the Bi-lstm_crfmentioning

confidence: 99%

Uyghur–Kazakh–Kirghiz Text Keyword Extraction Based on Morpheme Segmentation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The advantages of rule-based and stochastic techniques are combined in this strategy. In this method, words are rst probabilistically tagged after having post-processed with linguistic criteria to tag token (Besharati et al 2021; Divyapushpalakshmi and Ramalakshmi 2021). It is often more accurate to use this strategy than to use other strategies (Mohnot et al 2014).…”

Section: Pos Tagging Using Hybrid Approachmentioning

confidence: 99%

A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text

Sunita,

Kumar,

Neetika

2023

Preprint

View full text Add to dashboard Cite

Part-of-speech tagging faces unique difficulties when dealing with code-mixed social media text, which combines multiple languages in informal content created by users. In India, many web users employ a mixture of regional languages and English on platforms like Facebook, Instagram, and WhatsApp to express their messages and emotions. Text derived from social media is used in a variety of applications, such as speech recognition, machine learning, information retrieval, question answering, sentiment analysis, and named entity recognition. Due to training with monolingual texts, natural language processing tools such as part-of-speech taggers and parsers don't perform well. Assigning grammatical labels to individual words (such as verbs, adjectives, and nouns) is a critical task in natural language processing. This review paper extensively surveys the existing literature on part-of-speech tagging specifically developed for Indian and Foreign code-mixed social media text. We examine and categorize the approaches utilized in prior studies, taking into account the diverse techniques and methodologies employed to handle the complexities of code-mixed data. These approaches encompass rule-based methods, statistical models, and deep learning techniques such as recurrent neural networks and transformers. To enable comprehensive analysis, we compare and evaluate the performance of various state-of-the-art code-mixed part-of-speech taggers using benchmark datasets. We discuss the evaluation metrics used in these studies. Lastly, we explore the challenges introduced by noisy and informal language commonly found in code-mixed social media text. This review paper serves as a valuable resource for researchers and practitioners seeking to understand the current state of the art in code-mixed part-of-speech tagging for social media text. It offers insights into the strengths and weaknesses of existing approaches, identifies research gaps, and proposes potential avenues for future research to advance the field.

show abstract

“…In the beginning, randomly assigned weights are set at the beginning of algorithm training. Then, the MLP algorithm automatically performs weight changing to define the hidden layer unit representation is mostly good at minimizing the misclassification [54][55][56]. Besharati et al [54] proposed a POS tagging model for the Persian language using word vectors as the input for MLP and LSTM neural networks.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…Then, the MLP algorithm automatically performs weight changing to define the hidden layer unit representation is mostly good at minimizing the misclassification [54][55][56]. Besharati et al [54] proposed a POS tagging model for the Persian language using word vectors as the input for MLP and LSTM neural networks. Then the proposed model is compared with the results of the other neural network models and with a second-order HMM tagger, which is used as a benchmark.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

View full text Add to dashboard Cite

Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

show abstract

A hybrid statistical and deep learning based technique for Persian part of speech tagging

Cited by 12 publications

References 14 publications

Uyghur–Kazakh–Kirghiz Text Keyword Extraction Based on Morpheme Segmentation

Uyghur–Kazakh–Kirghiz Text Keyword Extraction Based on Morpheme Segmentation

A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text

Part of speech tagging: a systematic review of deep learning and machine learning approaches

Contact Info

Product

Resources

About