Two Level Disambiguation Model for Query Translation

Bajpai, Pratibha; Verma, Parul; Abbas, S. Qasim

doi:10.11591/ijece.v8i5.pp3923-3932

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Machine translation is an efficient form of translation that can translate anytime, anywhere, online or offline. Of course, there are also many limitations and translation problems [8,9]. For example, the translation is too rigid and lacks the style of the local culture, and there is also missed translation or repeated translation or translation errors (machine translation is a branch of computational linguistics, is one of the ultimate goals of artificial intelligence, and has important scientific research value; the process of machine translation can be divided into several steps: original text analysis, original translation conversion, and translation generation).…”

Section: 11mentioning

confidence: 99%

RNN Neural Network Model for Chinese-Korean Translation Learning

Dong

2022

Security and Communication Networks

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Translation is the expression of words into other forms so that they are mutually understandable. However, the current Chinese-Korean translation information conversion model is not mature enough, and there are many defects and errors. This paper proposes an improved RNN neural network translation model. The translation model proposed in this study is an upgraded RNN neural network (a form of recurrent neural network). A decoder matching mode is included in the model, allowing it to learn both alignment and transformation at the same time. This allows for quicker neural network training, resulting in increased translation speed and efficiency. In addition, this article compares the BLEU scores of the BilingualCorpus Chinese-Korean corpus I, II, and III with those of the standard translation model. The test results suggest that the enhanced RNN model described in this research has an average BLEU score of roughly 45 points. The conventional model’s average BLEU score is just approximately 30 points. The BLEU score of the model in this paper has increased by about 15 points, indicating that the translation quality of the translation model in this paper has been significantly improved.

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Section: 11mentioning

confidence: 99%

RNN Neural Network Model for Chinese-Korean Translation Learning

Dong

2022

Security and Communication Networks

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“…This stemmer can be used effectively to improve the query translation performance for information retrievalsystem [15]. Finally, we have integrated the new stemmer in the existing Dspace for Odia text retrieval system.…”

Section: Discussionmentioning

confidence: 99%

Development and Integration of an Odia Stemmer in Dspace for Odia Search Engine

Jena*,

Rautaray

2020

IJITEE

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Stemmer is used for reducing inflectional or derived word to its stem. This technique involves removing the suffix or prefix affixed in a word. It can be used for information retrieval system to refine the overall execution of the retrieval process. This process is not equivalent to morphological analysis. This process only finds the stem of a word. This technique decreases the number of terms in information retrieval system. There are various techniques exists for stemming. Here a new hybrid stemmer has developed named as “Mula” for Odia Language. It is a combination of brute force and enhanced suffix strippingapproach for Odia language. The new born stemmer is both computationally inexpensive and domain independent. We have integrated this stemmer in existing Dspace for Odia text retrieval System. The results are commendable and suggest that the new stemmer can be used effectively in Odia Search Engine. The proposed stemmer also handles over-stemming and understemming effectively

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“…Stemmer can be used in cross-language information retrieval [14] to reduce as many related words to a common form which is not in base form. Example suppose the user enter the query in English, it retrieves relevant document written in Odia.…”

Section: Cross-language Information Retrieval (Clir)mentioning

confidence: 99%

Design and implementation of an effective web-based hybrid stemmer for Odia language

Jena¹,

Rautaray²

2020

IJAAS

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<p><span>Stemmer is used for reducing inflectional or derived word to its stem. This technique involves removing the suffix or prefix affixed in a word. It can be used for information retrieval system to refine the overall execution of the retrieval process. This process is not equivalent to morphological analysis. This process only finds the stem of a word. This technique decreases the number of terms in information retrieval system. There are various techniques exists for stemming. In this paper, a new web-based stemmer has been proposed named as “Mula” for Odia Language. It uses the Hybrid approach (i.e. combination of brute force and suffix removal approach) for Odia language. The new born stemmer is both computationally faster and domain independent. The results are favourable and indicate that the proposed stemmer can be used effectively in Odia Information Retrieval systems. This stemmer also handles the problem of over-stemming and under-stemming in some extend.</span></p>

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Two Level Disambiguation Model for Query Translation

Cited by 5 publications

References 9 publications

RNN Neural Network Model for Chinese-Korean Translation Learning

RNN Neural Network Model for Chinese-Korean Translation Learning

Development and Integration of an Odia Stemmer in Dspace for Odia Search Engine

Design and implementation of an effective web-based hybrid stemmer for Odia language

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