A predictive typological content retrieval method for real-time application using multilingual natural language processing

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Abstract:

Natural language processing (NLP) is widely used in multi-media real-time applications for understanding human interactions through computer aided-analysis. NLP is

common in auto-filling, voice recognition, typo-checking applications, and so forth.

Multilingual NLP requires vast data processing and interaction recognition features

for leveraging content retrieval precision. To strengthen this concept, a predictive

typological content retrieval method is introduced in this article. The proposed

method maximizes and relies on distributed transfer learning for training multilingual

interactions with pitch and tone features. The phonetic pronunciation and the previous content-based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the

previous processing instances. For this purpose, the auto-fill and error correction

data are augmented with the training and multilingual processing databases.

Depending on the current prediction and previous content, the knowledge base is

updated, and further training relies on this feature. Therefore, the proposed method

accurately identifies the content across multilingual NLP models.

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