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Natural-Language-Toolkit for bahasa Malaysia, powered by Deep Learning.

Project description

https://raw.githubusercontent.com/DevconX/Malaya/master/session/towns-of-malaya.jpg

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Natural-Language-Toolkit for bahasa Malaysia, powered by Deep Learning Tensorflow.

Features

  • Entities Recognition, using latest state-of-art CRF deep learning models to do Naming Entity Recognition.

  • Language Detection, using various machine learning models to distinguish Malay, English, and Indonesian.

  • Normalizer, using local Malaysia NLP researches to normalize any bahasa texts.

  • Num2Word

  • Part-of-Speech Recognition, using latest state-of-art CRF deep learning models to do POS Recognition.

  • Sentiment Analysis, from BERT, Fast-Text, Dynamic-Memory Network, Attention to build deep sentiment analysis models.

  • Spell Correction, using local Malaysia NLP researches to auto-correct any bahasa words.

  • Stemmer

  • Subjectivity Analysis, from BERT, Fast-Text, Dynamic-Memory Network, Attention to build deep subjectivity analysis models.

  • Summarization, using skip-thought state-of-art to give precise summarization.

  • Topic Modelling, provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling.

  • Topic and Influencers Analysis, using deep and machine learning models to understand topics and Influencers similarity in sentences.

  • Toxicity Analysis, from BERT, Fast-Text, Dynamic-Memory Network, Attention to build deep Toxic Multi-label analysis models.

  • Word2Vec

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