text_similarity  by adsieg

Resources for text similarity methods

created 6 years ago
400 stars

Top 73.4% on sourcepulse

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Project Summary

This repository provides a comprehensive collection of methods and resources for computing text similarity, targeting NLP researchers and practitioners. It aims to offer a broad overview and practical implementations of various techniques, from traditional metrics to advanced deep learning models, enabling users to select and apply the most suitable approach for their specific needs.

How It Works

The project explores a wide array of text similarity algorithms, including statistical methods like Jaccard Similarity, TF-IDF, and Latent Semantic Analysis (LSA), as well as word embedding-based approaches such as Word2Vec, GloVe, and fastText combined with metrics like Cosine Similarity and Word Mover's Distance (WMD). It also delves into deep learning models like Variational Autoencoders (VAEs), Universal Sentence Encoder (USE), and Siamese LSTMs, often leveraging pre-trained models and contextual embeddings (e.g., ELMo, BERT). The underlying principle is to represent text semantically and then quantify the distance or similarity between these representations.

Quick Start & Requirements

  • Install: Not explicitly detailed, but likely involves standard Python packages.
  • Prerequisites: Python, NLP libraries (e.g., NLTK, spaCy, Gensim, TensorFlow/Keras, PyTorch), potentially pre-trained models or large datasets for some methods.
  • Resources: Setup complexity and resource requirements will vary significantly based on the chosen similarity method, with deep learning models demanding GPUs and substantial memory.
  • Links: The README extensively links to external articles, tutorials, and GitHub repositories for detailed explanations and implementations of each method.

Highlighted Details

  • Extensive coverage of both traditional and state-of-the-art NLP similarity techniques.
  • Includes implementations and discussions of various embedding methods (Word2Vec, GloVe, fastText, ELMo, USE, BERT).
  • Explores advanced distance metrics like Word Mover's Distance (WMD) and Earth Mover's Distance.
  • Features deep learning architectures such as Siamese LSTMs and Variational Autoencoders (VAEs).

Maintenance & Community

The repository appears to be a curated collection of resources rather than an actively maintained project with a dedicated community. It primarily serves as a reference and learning hub, with numerous links to external tutorials and related GitHub projects.

Licensing & Compatibility

The licensing is not explicitly stated in the README. Given the extensive use of external resources and libraries, users should verify the licenses of individual components and linked projects for compatibility, especially for commercial use.

Limitations & Caveats

The README is a comprehensive list of topics and external links rather than a self-contained project with runnable code. Users will need to navigate and potentially integrate code from various external sources, which may require significant effort to set up and use consistently. The project itself does not appear to offer a unified API or a single installation command.

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5 years ago

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