DeepK/hoDMD-experiments
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition
This project helps natural language processing (NLP) researchers and data scientists analyze sentence meaning by transforming text into numerical representations. It takes raw text sentences and pretrained word embeddings as input, then generates detailed numerical 'spectral' embeddings that capture deeper linguistic patterns. These embeddings can then be used to compare sentences or feed into other machine learning models for tasks like sentiment analysis or information retrieval.
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Use this if you are a natural language processing researcher looking for advanced methods to create rich, spectral sentence embeddings for your text analysis tasks.
Not ideal if you need a simple, off-the-shelf sentence embedding solution without deep technical setup or are not familiar with scientific NLP research.
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4
Language
Python
License
BSD-2-Clause
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Last pushed
Jul 27, 2019
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