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Malaya is a Natural-Language-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.

Documentation

Proper documentation is available at https://malaya.readthedocs.io/

Installing from the PyPI

CPU version

$ pip install malaya

GPU version

$ pip install malaya-gpu

Only Python 3.6.x and above and Tensorflow 1.X are supported.

Features

• Emotion Analysis

  From fine-tuning BERT, Attention-Recurrent model, Sparse Tensorflow, Self-Attention to build deep emotion analysis models.

• Entities Recognition

  Latest state-of-art CRF deep learning models to do Naming Entity Recognition.

• Language Detection

  using Multinomial, SGD, XGB, Fast-text N-grams deep learning to distinguish Malay, English, and Indonesian.

• Normalizer

  using local Malaysia NLP researches to normalize any bahasa texts.

• Num2Word

  Convert from numbers to cardinal or ordinal representation.

• Part-of-Speech Recognition

  Latest state-of-art CRF deep learning models to do Part-of-Speech Recognition.

• Dependency Parsing

  Latest state-of-art CRF deep learning models to do analyzes the grammatical structure of a sentence, establishing relationships between words.

• ELMO (biLM)

  Provide pretrained bahasa wikipedia and bahasa news ELMO, with easy interface and visualization.

• Relevancy Analysis

  From Dilated Convolutional Neural Network and Self-Attention to build deep relevancy analysis models.

• Sentiment Analysis

  From fine-tuning BERT, Attention-Recurrent model, Sparse Tensorflow and Self-Attention to build deep sentiment analysis models.

• Spell Correction

  Using local Malaysia NLP researches to auto-correct any bahasa words.

• Stemmer

  Use Character LSTM Seq2Seq with attention state-of-art to do Bahasa stemming.

• Subjectivity Analysis

  From fine-tuning BERT, Attention-Recurrent model, Sparse Tensorflow and Self-Attention to build deep subjectivity analysis models.

• Similarity

  Use deep LSTM siamese, deep Dilated CNN siamese, deep Self-Attention, siamese, Doc2Vec and BERT to build deep semantic similarity models.

• Summarization

  Using skip-thought and residual-network with attention state-of-art, LDA, LSA and Doc2Vec to give precise unsupervised summarization, and TextRank as scoring algorithm.

• Topic Modelling

  Provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling with topics visualization.

• Toxicity Analysis

  From fine-tuning BERT, Attention-Recurrent model, Self-Attention to build deep toxicity analysis models.

• Word2Vec

  Provide pretrained bahasa wikipedia and bahasa news Word2Vec, with easy interface and visualization.

• Fast-text

  Provide pretrained bahasa wikipedia Fast-text, with easy interface and visualization.

References

If you use our software for research, please cite:

@misc{Malaya, Natural-Language-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow,
  author = {Husein, Zolkepli},
  title = {Malaya},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huseinzol05/malaya}}
}

License