mlsanityData to 14 Oct 2026

Measuring Word Significance using Distributed Representations of Words

arXiv 1508.02297 ↗First posted 10 Aug 2015cs.CLcited by 21

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Abstract (arXiv)

Distributed representations of words as real-valued vectors in a relatively low-dimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Aug 2015.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 2; 2024: 6; 2025: 1; 2026: 3.

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  1. 2021CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directionscited by 78
  2. 2021Incorporating Residual and Normalization Layers into Analysis of Masked Language Modelscited by 51
  3. 2022A Simple Hash-Based Early Exiting Approach For Language Understanding and Generationcited by 32
  4. 2021Variational Graph Normalized Auto-Encoderscited by 29
  5. 2023CMOT: Cross-modal Mixup via Optimal Transport for Speech Translationcited by 22
  6. 2022Norm of Word Embedding Encodes Information Gaincited by 21
  7. 2024Revisiting Cosine Similarity via Normalized ICA-transformed Embeddingscited by 5
  8. 2021Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddingscited by 4
  9. 2022Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappingscited by 4
  10. 2024Importance-Aware Data Augmentation for Document-Level Neural Machine Translationcited by 4
  11. 2021Monitoring geometrical properties of word embeddings for detecting the emergence of new topicscited by 3
  12. 2022Probing with Noise: Unpicking the Warp and Weft of Embeddingscited by 3
9 more
  1. 2024Zipfian Whiteningcited by 3
  2. 2023Assessing Word Importance Using Models Trained for Semantic Taskscited by 2
  3. 2026Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilingualitycited by 2
  4. 2024Norm of Mean Contextualized Embeddings Determines their Variancecited by 1
  5. 2025Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimensioncited by 1
  6. 2024Learning Word Embedding with Better Distance Weighting and Window Size Schedulingcited by 0
  7. 2024In Tree Structure Should Sentence Be Generatedcited by 0
  8. 2026LIDS: LLM Summary Inference Under the Layered Lenscited by 0
  9. 2026N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimizationcited by 0