rz1993 / Enelpy

A collection of notebooks and model implementations for various NLP tasks

Enelpy

A series of natural language processing notebooks and implemented models for various tasks from low-level tasks, such as stemming, lemmatization, POS-tagging, to high level tasks such as sentiment analysis and summarization. Introduces a variety of models for these tasks from rule-based to traditional ML models to RNNs.

Notebooks

• Stemming and lemmatization in progress
• POS tagging and Named Entity Recognition in progress
• Word Embeddings in progress
• Language models in progress
• Syntactic Parsing (Dependency and CFG Parsing) in progress
• Sentiment and Topic Modeling in progress
• Summarization in progress

Models Implemented

• Averaged Perceptron POS tagger
• Latent Dirichlet Allocation via Collapsed Gibbs Sampler

Neural Models

• word2vec with negative sampling, subsampling and adjustable context windows
• LSTM + Linear Chain CRF for named entity recognition

Libraries and tools used

• spaCy - for abstracting low level tasks in higher level ones
• scikit-learn - for implementing feature extraction and machine learning models
• tensorflow - for implementing deep learning models
• keras - for higher level deep learning model implementation
• matplotlib - for visualizations