Indowordnet’s help in Indian language machine translation

Sreelekha, S; Bhattacharyya, Pushpak

doi:10.1007/s00146-019-00907-w

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The project aimed to help student immigrants in Lampung province to translate the Lampung dialect of Nyo through the model and the proposed method was adopted as a working model with an accuracy rate of 59.85%. Sreelekha and Bhattacharyya [8] provided a solution for machine translation of Indian languages where digital resources are scarce by using Indowordnet lexical database to extend statistical language models and evaluate 440 models for 110 pairs of languages for comparison. They found that using lexical database mapping helped to resolve linguistic ambiguities and improve translation quality.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The two sub-models are embedded in the minimum error training with linear interpolation, and the optimal results are re-output using linear re-ordering. Specifically, when the statistical language model based on minimum error training www.ijacsa.thesai.org outputs the ranking results, the two sub-models process the output word order with probability calculation, and then the probability calculation results are linearly interpolated with the ranking results of the statistical language model, as shown in (8).…”

Section: A a Statistical Language Model-based Algorithm Formentioning

confidence: 99%

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Statistical Language Model-based Analysis of English Corpora and Literature

Chai

2023

IJACSA

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Despite widespread use of statistical language models in language processing, their ability to process natural languages is not advanced and they struggle to effectively capture linguistic information. Furthermore, there is a lack of automatic processing models in the field of natural language processing. In order to address these issues, and Improve the processing ability of statistical language models for English language a statistical language model optimization algorithm has been proposed. This algorithm is based on an improved resorting algorithm and is specifically applied to process English literary texts. Experimental results indicate that the proposed algorithm outperforms the N-gram algorithm in a majority of texts, with a maximum accuracy improvement of 14.5%. Additionally, in terms of the grammar analysis model, there is a high level of consistency between the model's scoring and the expert manpower scoring, as reflected by a correlation coefficient of 0.7893. This high level of consistency between the grammar analysis model and expert analysis results holds significant importance for the advancement of natural language processing.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: A a Statistical Language Model-based Algorithm Formentioning

confidence: 99%

Statistical Language Model-based Analysis of English Corpora and Literature

Chai

2023

IJACSA

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“…Where n w is the number of sentences in which w occurs and N is the total number of sentences in the document. The TF part remains same, which is calculated as follows (2) Where f(w,s) is the number of times w appears in s and n s is the total number of words in the sentence s. The value for each cell is obtained by multiplying the value obtained in equation (1) with that of equation 2.…”

Section: A Word-sentence Matrixmentioning

confidence: 99%

“…But, like most other Indic languages, Assamese is a resource-poor language which lacks sufficient resources and tools required for advanced natural language processing tasks such as automatic summarization, machine translation etc. [1] [2]. On the other hand, the rapid growth of Assamese content on the web and other digital platforms has necessitated the development of automatic text summarization system (ATS) for Assamese.…”

Section: Introductionmentioning

confidence: 99%

Single Document Text Summarization of a Resource-Poor Language using an Unsupervised Technique

Chetia,

Hazarika

2019

IJEAT

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Automatic text summarization of a resource-poor language is a challenging task. Unsupervised extractive techniques are often preferred for such languages due to scarcity of resources. Latent Semantic Analysis (LSA) is an unsupervised technique which automatically identifies semantically important sentences from a text document. Two methods based on Latent Semantic Analysis have been evaluated on two datasets of a resource-poor language using Singular Value Decomposition (SVD) on different vector-space models. The performance of the methods is evaluated using ROUGE-L scores obtained by comparing the system generated summaries with human generated model summaries. Both the methods are found to be performing better for shorter documents than longer ones.

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Indowordnet’s help in Indian language machine translation

Cited by 2 publications

References 8 publications

Statistical Language Model-based Analysis of English Corpora and Literature

Statistical Language Model-based Analysis of English Corpora and Literature

Single Document Text Summarization of a Resource-Poor Language using an Unsupervised Technique

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