Malaya

1. Added deep siamese network, https://malaya.readthedocs.io/en/latest/Similarity.html#deep-siamese-network.
2. Added BERT deep siamese network, https://malaya.readthedocs.io/en/latest/Similarity.html#bert-model
3. Added Doc2Vec to calculate semantic similarity, https://malaya.readthedocs.io/en/latest/Similarity.html#calculate-similarity-using-doc2vec
4. Now all extractive summarization is use TextRank algorithm as scoring algorithm.
5. Added Doc2Vec for extractive summarization, https://malaya.readthedocs.io/en/latest/Summarization.html#load-doc2vec-summarization

1. Added relevancy analysis, to study an article or a piece of text is relevant, tendency to become a fake news. https://malaya.readthedocs.io/en/latest/Relevancy.html
2. Added visualization dashboard for emotion analysis, relevancy analysis, sentiment analysis, subjectivity analysis and toxicity analysis. Very easy to use, call `predict_words` function and it will popup.
3. Added neutral class for relevancy analysis, sentiment analysis and subjectivity analysis.
4. Use Malaya preprocessing for all deep learning models classification.

1. Fix some english loading bugs
2. Added clustering visualization, https://malaya.readthedocs.io/en/latest/Cluster.html
3. Added text augmentation, https://malaya.readthedocs.io/en/latest/Generator.html
4. Normalizer and Spelling now able to detect english words.

1. Added text similarity and released partial topics related, https://malaya.readthedocs.io/en/latest/Similarity.html
2. Added word-mover distance interface, https://malaya.readthedocs.io/en/latest/Mover.html
3. Added pretrained fast-text based on wikipedia, https://malaya.readthedocs.io/en/latest/Fasttext.html
4. Improve sentiment analysis, trained on more than 800k sentences and more sensitive towards social media texts.
5. Remove n-grams for all fast-text models to reduce dimension curse.
6. Remove sparse limit for all fast-text-char models to improve n-grams sensitivity.

1. Available to check deep learning models available for stemming, simply, `malaya.stem.available_deep_model()`.
2. Available to load deep learning model for stemming, simply, `malaya.stem.malaya.stem.deep_model()`, https://malaya.readthedocs.io/en/latest/Stemmer.html
3. Improve dependency parsing documentation, https://malaya.readthedocs.io/en/latest/Dependency.html#dependency-graph-object

1. Retrained Entities recognition models.
2. Retrained POS recognition models.
3. Able to print important features from deep entities recognition models, simply `model.print_features()`.
4. Able to print important transitions from deep entities recognition models, simply `model.print_transitions()`.
5. Able to print important features from deep POS recognition models, simply `model.print_features()`.
6. Able to print important transitions from deep POS recognition models, simply `model.print_transitions()`.
7. Released Dependency Parsing features, https://malaya.readthedocs.io/en/latest/Dependency.html.