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This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.
A command-line utility for tagging part of speech for words in text.
A convenient Chinese word segmentation tool 简便中文分词器
Use the famous language model, xlnet, to do sequence tagging/ sequence labelling/ named entity recognition(NER) / noun extraction;
Part-of-speech tagger with character-level features using PyTorch
Machine Learning models
Part-Of-Speech-tagging using Hidden Markov model to identify the category of words ('noun', 'verb', ...) in plain text.
My journey of learning NLP. Contains info about Basic NLP techniques, Hugging Face , GPT3and Spacy
Practice Repository for Natural Language tasks!
Using Markov chains and NLTK to create a text-generation machine learning model.
Exploring sentiments of financial news on Malaysia markets using n-grams and parts of speech (POS) with Python.
Part of Speech tagging from treebank corpus using Hidden Markov Model approach
Implemented machine learning models like Bayes, HMM & MCMC from scratch for POS tagging using Python with 94% accuracy
My legal background gave me a deep appreciation for language's importance. It's not just words; it's a profound understanding woven into every case. This connection led me to coding, where I coded a potent pipeline system with Stanford CoreNLP.
Comparing approaches to POS tagging
Machine Learning Brute-force approach on Part of Speech Tagging. Estimated precision, recall, accuracy, and F-measure on the task using 5-fold cross-validation. Implemented classifiers in Python for extra credit. Completed for school.
Arabic part of speech tagging using arabic PUD dataset using bidirectioanl LSTM for sequential labeling classification
Natural-Language-Processing
A Deep Dive into Gender Representation in Netflix's Films
Aspect based sentiment analysis in tunisian dialect
Evaluating LSTM and GRU Part-of-speech taggers' performance on 6 different languages.
Contains work done for NLP Specialization courses from DeepLearning.AI