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Static Fuzzy Bag-of-Words: a lightweight sentence embedding algorithm

arXiv 2304.03098 ↗First posted 6 Apr 2023cs.CL stat.MLcited by 0

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Abstract (arXiv)

The introduction of embedding techniques has pushed forward significantly the Natural Language Processing field. Many of the proposed solutions have been presented for word-level encoding; anyhow, in the last years, new mechanism to treat information at an higher level of aggregation, like at sentence- and document-level, have emerged. With this work we address specifically the sentence embeddings problem, presenting the Static Fuzzy Bag-of-Word model. Our model is a refinement of the Fuzzy Bag-of-Words approach, providing sentence embeddings with a predefined dimension. SFBoW provides competitive performances in Semantic Textual Similarity benchmarks, while requiring low computational resources.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 6 Apr 2023.

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16 of 43 references.

References · 16

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4 more
  1. 2015A Neural Network Approach to Context-Sensitive Generation of Conversational Responsescited by 98
  2. 2019Better Word Embeddings by Disentangling Contextual n-Gram Informationcited by 12
  3. 2018Parameter-free Sentence Embedding via Orthogonal Basiscited by 11
  4. 2019Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectorscited by 7

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