2020
EMNLP
EMNLP 2020
Going Beyond T-SNE: Exposing whatlies in Text Embeddings
Abstract
AbstractWe introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://rasahq.github.io/whatlies/.
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Interdisciplinary Bridge
— Computer Science and Deep Learning and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— text embedding
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Embedding Learning
Natural Language Processing > Resources & Methods > Text Representation
Machine Learning > Core Methods > Dimensionality Reduction
Computer Science > Applications > Computer Graphics
Deep Learning > Learning Types > Representation Learning