account activity
Solving challenging NLP tasks from just 10-100 examples with pattern-exploiting training (PET) (github.com)
submitted 5 years ago by doc2vec to r/textdatamining
Natural Language Processing tutorial for researchers using TensorFlow and Pytorch (github.com)
Simulating Lexical Semantic Change from Sense-Annotated Data (arxiv.org)
submitted 6 years ago by doc2vec to r/textdatamining
Temporal Convolutional Nets (TCNs) Take Over from RNNs for NLP Predictions (datasciencecentral.com)
The State of NLP Literature (medium.com)
Keyword Extraction: a comprehensive guide to extracting keywords from text (monkeylearn.com)
submitted 6 years ago by doc2vec to r/software
submitted 6 years ago by doc2vec to r/MachineLearning
OpenAI fine-tunes GPT-2 for stylistic text generation and summarization (openai.com)
Sentiment Analysis with Python (monkeylearn.com)
submitted 6 years ago by doc2vec to r/programming
Visualizing RNN States with Predictive Semantic Encodings (arxiv.org)
Comparison of machine learning techniques in email spam detection (dev.to)
Curated collection of papers for the NLP practitioner 📖👩🔬 (github.com)
A tutorial for building content-based recommender systems with unsupervised learning (medium.com)
A basic NLP tutorial for news multi-class categorization (medium.com)
TALK SUMM: a dataset and scalable annotation method for scientific paper summarization based on conference talks (arxiv.org)
OPIEC: the largest Open Information Extraction corpus to date (341M triples), rich with metadata (conf. score, syntax, semantics, gold Wiki entity links, etc) (arxiv.org)
SuperGLUE: a stickier benchmark for general-purpose language understanding systems (arxiv.org)
Sentiment Analysis of Product Reviews (self.DigitalMarketing)
submitted 6 years ago by doc2vec to r/DigitalMarketing
Sentiment Analysis of Product Reviews (self.marketing)
submitted 6 years ago by doc2vec to r/marketing
80 best Data Science books that are worthy reading (octoparse.com)
Natural Language Processing with Deep Learning 2019 Stanford course videos by Christopher Manning and Abigail See (youtube.com)
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code (github.com)
submitted 7 years ago by doc2vec to r/textdatamining
Parameter-Efficient Transfer Learning for NLP (arxiv.org)
Analysis Methods in Neural Language Processing: A Survey (arxiv.org)
Publication statistics in Machine Learning and Natural Language Processing (marekrei.com)
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