• ActiveLoop
    • Solutions

      INDUSTRIES

      • agricultureAgriculture
        agriculture_technology_agritech
      • audioAudio Processing
        audio_processing
      • roboticsAutonomous & Robotics
        autonomous_vehicles
      • biomedicalBiomedical & Healthcare
        Biomedical_Healthcare
      • multimediaMultimedia
        multimedia
      • safetySafety & Security
        safety_security

      CASE STUDIES

      • IntelinAir
      • Learn how IntelinAir generates & processes datasets from petabytes of aerial imagery at 0.5x the cost

      • Earthshot Labs
      • Learn how Earthshot increased forest inventory management speed 5x with a mobile app

      • Ubenwa
      • Learn how Ubenwa doubled ML efficiency & improved scalability for sound-based diagnostics

      ​

      • Sweep
      • Learn how Sweep powered their code generation assistant with serverless and scalable data infrastructure

      • AskRoger
      • Learn how AskRoger leveraged Retrieval Augmented Generation for their multimodal AI personal assistant

      • TinyMile
      • Enhance last mile delivery robots with 10x quicker iteration cycles & 30% lower ML model training cost

      Company
      • About
      • Learn about our company, its members, and our vision

      • Contact Us
      • Get all of your questions answered by our team

      • Careers
      • Build cool things that matter. From anywhere

      Docs
      Resources
      • blogBlog
      • Opinion pieces & technology articles

      • tutorialTutorials
      • Learn how to use Activeloop stack

      • notesRelease Notes
      • See what's new?

      • newsNews
      • Track company's major milestones

      • langchainLangChain
      • LangChain how-tos with Deep Lake Vector DB

      • glossaryGlossary
      • Top 1000 ML terms explained

      • deepDeep Lake Academic Paper
      • Read the academic paper published in CIDR 2023

      • deepDeep Lake White Paper
      • See how your company can benefit from Deep Lake

      Pricing
  • Log in
image
    • Back
    • Share:

    Word Mover's Distance (WMD)

    Word Mover's Distance (WMD) is a powerful technique for measuring the semantic similarity between two text documents, taking into account the underlying geometry of word embeddings.

    WMD has been widely studied and improved upon in recent years. One such improvement is the Syntax-aware Word Mover's Distance (SynWMD), which incorporates word importance and syntactic parsing structure to enhance sentence similarity evaluation. Another approach, Fused Gromov-Wasserstein distance, leverages BERT's self-attention matrix to better capture sentence structure. Researchers have also proposed methods to speed up WMD and its variants, such as the Relaxed Word Mover's Distance (RWMD), by exploiting properties of distances between embeddings.

    Recent research has explored extensions of WMD, such as incorporating word frequency and the geometry of word vector space. These extensions have shown promising results in document classification tasks. Additionally, the WMDecompose framework has been introduced to decompose document-level distances into word-level distances, enabling more interpretable sociocultural analysis.

    Practical applications of WMD include text classification, semantic textual similarity, and paraphrase identification. Companies can use WMD to analyze customer feedback, detect plagiarism, or recommend similar content. One case study involves using WMD to explore the relationship between conspiracy theories and conservative American discourses in a longitudinal social media corpus.

    In conclusion, WMD and its variants offer valuable insights into text similarity and have broad applications in natural language processing. As research continues to advance, we can expect further improvements in performance, efficiency, and interpretability.

    Word Mover's Distance (WMD) Further Reading

    1.Re-evaluating Word Mover's Distance http://arxiv.org/abs/2105.14403v3 Ryoma Sato, Makoto Yamada, Hisashi Kashima
    2.Moving Other Way: Exploring Word Mover Distance Extensions http://arxiv.org/abs/2202.03119v2 Ilya Smirnov, Ivan P. Yamshchikov
    3.SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation http://arxiv.org/abs/2206.10029v1 Chengwei Wei, Bin Wang, C. -C. Jay Kuo
    4.Improving word mover's distance by leveraging self-attention matrix http://arxiv.org/abs/2211.06229v1 Hiroaki Yamagiwa, Sho Yokoi, Hidetoshi Shimodaira
    5.Speeding up Word Mover's Distance and its variants via properties of distances between embeddings http://arxiv.org/abs/1912.00509v2 Matheus Werner, Eduardo Laber
    6.WMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover's Distance in Sociocultural Analysis http://arxiv.org/abs/2110.07330v1 Mikael Brunila, Jack LaViolette
    7.Text classification with word embedding regularization and soft similarity measure http://arxiv.org/abs/2003.05019v1 Vít Novotný, Eniafe Festus Ayetiran, Michal Štefánik, Petr Sojka
    8.An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distance http://arxiv.org/abs/2005.06727v3 Jesmin Jahan Tithi, Fabrizio Petrini
    9.Wasserstein-Fisher-Rao Document Distance http://arxiv.org/abs/1904.10294v2 Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao
    10.A New Parallel Algorithm for Sinkhorn Word-Movers Distance and Its Performance on PIUMA and Xeon CPU http://arxiv.org/abs/2107.06433v3 Jesmin Jahan Tithi, Fabrizio Petrini

    Word Mover's Distance (WMD) Frequently Asked Questions

    What is Word Mover's Distance (WMD)?

    Word Mover's Distance (WMD) is a technique used to measure the semantic similarity between two text documents. It takes into account the underlying geometry of word embeddings, which are vector representations of words that capture their meanings. By comparing the distances between word embeddings in two documents, WMD can determine how similar the documents are in terms of their semantic content.

    How does WMD work?

    WMD works by leveraging pre-trained word embeddings, such as Word2Vec or GloVe, to represent words as vectors in a high-dimensional space. It then calculates the minimum "transportation cost" required to transform one document's word embeddings into another document's word embeddings. This transportation cost is based on the Earth Mover's Distance (EMD), a measure used in optimal transport theory. The lower the cost, the more similar the two documents are in terms of their semantic content.

    What are some improvements and variants of WMD?

    There have been several improvements and variants of WMD proposed in recent years. Some notable examples include: 1. Syntax-aware Word Mover's Distance (SynWMD): This method incorporates word importance and syntactic parsing structure to enhance sentence similarity evaluation. 2. Fused Gromov-Wasserstein distance: This approach leverages BERT's self-attention matrix to better capture sentence structure. 3. Relaxed Word Mover's Distance (RWMD): This method speeds up WMD by exploiting properties of distances between embeddings, providing a faster approximation of the original WMD.

    What are some practical applications of WMD?

    WMD has various practical applications in natural language processing, including: 1. Text classification: WMD can be used to classify documents into categories based on their semantic content. 2. Semantic textual similarity: WMD can measure the similarity between two sentences or documents, which is useful for tasks like paraphrase identification or document clustering. 3. Analyzing customer feedback: Companies can use WMD to analyze customer reviews and feedback, identifying common themes and sentiments. 4. Plagiarism detection: WMD can help detect instances of plagiarism by comparing the semantic similarity between documents. 5. Content recommendation: WMD can be used to recommend similar content to users based on their interests and preferences.

    What is the relationship between WMD and Earth Mover's Distance (EMD)?

    Earth Mover's Distance (EMD) is a measure used in optimal transport theory to calculate the minimum "transportation cost" required to transform one distribution into another. WMD is an adaptation of EMD for natural language processing tasks, specifically for measuring the semantic similarity between text documents. WMD leverages the underlying geometry of word embeddings and uses EMD to compute the transportation cost between the word embeddings of two documents.

    How does recent research extend WMD?

    Recent research has explored extensions of WMD by incorporating additional information, such as word frequency and the geometry of word vector space. These extensions have shown promising results in document classification tasks. Additionally, the WMDecompose framework has been introduced to decompose document-level distances into word-level distances, enabling more interpretable sociocultural analysis. As research continues to advance, we can expect further improvements in performance, efficiency, and interpretability of WMD and its variants.

    Explore More Machine Learning Terms & Concepts

cubescubescubescubescubescubes
  • Subscribe to our newsletter for more articles like this
  • deep lake database

    Deep Lake. Database for AI.

    • Solutions
      AgricultureAudio ProcessingAutonomous Vehicles & RoboticsBiomedical & HealthcareMultimediaSafety & Security
    • Company
      AboutContact UsCareersPrivacy PolicyDo Not SellTerms & Conditions
    • Resources
      BlogDocumentationDeep Lake WhitepaperDeep Lake Academic PaperHumans in the Loop Podcast
  • Tensie

    Featured by

    featuredfeaturedfeaturedfeatured