Syntax parsing model of Kazakh simple sentences

Sharipbay, Altynbek; Razakhova, Bibigul; Mukanova, Assel; Yergesh, Banu; Yelibayeva, Gaziza

doi:10.1145/3368691.3368745

Cited by 15 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods for creating basic sentences in Kazakh were laid out in [23][24][25][26][27]. It was feasible to automate text parsing using these models [18,28] by using Chomsky's context-free grammar (CFG) to formalize the syntax of uncomplicated sentences in the Kazakh language and create ontological models of these rules within the Protégé environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Amangeldy

Ukenova

Bekmanova

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This article is devoted to solving the problem of converting sign language into a consistent text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation. The paper proposes an improved method of continuous recognition of sign language, the results of which are transmitted to a natural language processor based on analyzers of morphology, syntax, and semantics of the Kazakh language, including morphological inflection and the construction of an intonation model of simple sentences. This approach has significant practical and social significance, as it can lead to the development of technologies that will help people with disabilities to communicate and improve their quality of life. As a result of the cross-validation of the model, we obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation. We also identified 20 sentence structures of the Kazakh language with their intonational model.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The approach relies on morphological rules and an ontological model to achieve this analysis. Another study [27] uses morphological principles to perform sentiment analysis on Kazakh phrases. Additionally, the paper [28] suggests an examination of the semantics of basic Kazakh sentences through syntactic analysis.…”

Section: Related Workmentioning

confidence: 99%

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Amangeldy

Ukenova

Bekmanova

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The works in [41] describe methods for determining the polarity of the text. When analyzing sentiment and emotion, the results of morphological [55,56] and syntactic analysis of the Kazakh language [57][58][59] are used.…”

Section: Current Alphabetmentioning

confidence: 99%

Emotional Speech Recognition Method Based on Word Transcription

Bekmanova

Yergesh

Sharipbay

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The emotional speech recognition method presented in this article was applied to recognize the emotions of students during online exams in distance learning due to COVID-19. The purpose of this method is to recognize emotions in spoken speech through the knowledge base of emotionally charged words, which are stored as a code book. The method analyzes human speech for the presence of emotions. To assess the quality of the method, an experiment was conducted for 420 audio recordings. The accuracy of the proposed method is 79.7% for the Kazakh language. The method can be used for different languages and consists of the following tasks: capturing a signal, detecting speech in it, recognizing speech words in a simplified transcription, determining word boundaries, comparing a simplified transcription with a code book, and constructing a hypothesis about the degree of speech emotionality. In case of the presence of emotions, there occurs complete recognition of words and definitions of emotions in speech. The advantage of this method is the possibility of its widespread use since it is not demanding on computational resources. The described method can be applied when there is a need to recognize positive and negative emotions in a crowd, in public transport, schools, universities, etc. The experiment carried out has shown the effectiveness of this method. The results obtained will make it possible in the future to develop devices that begin to record and recognize a speech signal, for example, in the case of detecting negative emotions in sounding speech and, if necessary, transmitting a message about potential threats or riots.

show abstract

“…Manually handcrafting language syntax into grammar rules requires indepth knowledge of a language and extensive labour. It may be difficult to cover an entire language structure as natural languages are complex but may serve as a starting point for languages where no Treebanks are available (Ababou et al, 2017;Korzeniowski and Mazurkiewicz, 2017;Þorsteinsson et al, 2019;Sharipbay et al, 2019;Kapanadze, 2019;Dorđević and Stojković, 2020).…”

Section: Related Workmentioning

confidence: 99%

A Context-Free Grammar for Parsing Manipuri Language

Nirmal¹,

Sharma²

2021

Journal of Computer Science

View full text Add to dashboard Cite

Parsing, i.e., identifying the underlying hierarchical structure of natural language expressions is important for several natural language processing applications. In recent times Machine Learning (ML) approaches have been developed for this study for many languages. Most of the effective techniques require an annotated corpus of the language for training and validation. For the Manipuri language of the Tibeto-Burman family, neither such a corpus nor a grammar framework to automatically analyse and represent the structure of sentences exists yet. This study proposes a context-Free Grammar (CFG) that provides the framework to represent the structure of Manipuri sentences. This paves the way for parsing Manipuri sentences using CFG-based parsers for various applications and to conveniently build a Treebank for developing ML-based parsers for Manipuri. The rules of the proposed CFG are handcrafted after extensive analysis of the structure of Manipuri sentences. The grammar covers simple, compound, complex and compound-complex sentences. For evaluation, we induce an Earley's parser with the proposed CFG and test it over a collection of sentences that covers the possible varieties of structure. A recognition rate of 83.20% achieved in these experiments indicates the effectiveness of the proposed grammar.

show abstract

Syntax parsing model of Kazakh simple sentences

Cited by 15 publications

References 16 publications

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Emotional Speech Recognition Method Based on Word Transcription

A Context-Free Grammar for Parsing Manipuri Language

Contact Info

Product

Resources

About