How to solve 90% of NLP problems: a step-by-step guide
Using Machine Learning to understand and leverage text.
nlp
Deep Learning Research Review Week 3: Natural Language Processing – Adit De
Engineering at Forward | UCLA CS '19
NLP Concepts with spaCy. Code examples released under CC0 https://creativec
NLP Concepts with spaCy. Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ · GitHub
agnusmaximus/Word2Bits: Quantized word vectors that take 8x-16x less space than regular word vectors
Quantized word vectors that take 8x-16x less space than regular word vectors - agnusmaximus/Word2Bits
5 Fantastic Practical Natural Language Processing Resources
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
NLTK 3.3 is out
NLTK 3.3 has been released NLTK 3.3 includes the following: Support Python 3.6 New interface to CoreNLP Support synset retrieval by sense key Minor…
Topic Modeling with Gensim (Python) - A Practical Guide
Topic Modeling is a technique to understand and extract the hidden topics from large volumes of text. Latent Dirichlet Allocation(LDA) is an algorithm for topic modeling, which has excellent implementations in the Python's Gensim package. This tutorial tackles the problem of…
Understanding what is behind Sentiment Analysis (Part II)
Fine-tuning our sentiment classifier
Understanding Feature Engineering (Part 3) — Traditional Methods for Text D
Traditional strategies for taming unstructured, textual data
Google's trained Word2Vec model in Python · Chris McCormick
Gensim: topic modelling for humans
Efficient topic modelling in Python
concrete_NLP_tutorial/NLP_notebook.ipynb at master · hundredblocks/concrete
An NLP workshop about concrete solutions to real problems - hundredblocks/concrete_NLP_tutorial
Prodigy - Radically efficient machine teaching
A downloadable annotation tool for LLMs, NLP and computer vision tasks such as named entity recognition, text classification, object detection, image segmentation, evaluation and more.
spacy-notebooks/lightning_tour.ipynb at master · explosion/spacy-notebooks
💫 Jupyter notebooks for spaCy examples and tutorials - explosion/spacy-notebooks
Word Tensors
Counting and tensor decompositions are elegant and straightforward techniques. But these methods are grossly underepresented in business contexts. In this p...
Topic Modeling with LDA Introduction
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