The Effects of Data Size and Frequency Range on Distributional Semantic Models
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Abstract (arXiv)
This paper investigates the effects of data size and frequency range on distributional semantic models. We compare the performance of a number of representative models for several test settings over data of varying sizes, and over test items of various frequency. Our results show that neural network-based models underperform when the data is small, and that the most reliable model over data of varying sizes and frequency ranges is the inverted factorized model.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2016.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1.
6 papers.
- 2021A comparative evaluation and analysis of three generations of Distributional Semantic Modelscited by 19
- 2021Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representationcited by 8
- 2021Efficient Multi-Modal Embeddings from Structured Datacited by 3
- 2022Quantifying Gender Bias in Consumer Culturecited by 2
- 2023Witgenstein's influence on artificial intelligencecited by 1
- 2024Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Languagecited by 0
- 2024Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language17 Oct 2024
- 2023Witgenstein's influence on artificial intelligence3 Feb 2023
- 2022Quantifying Gender Bias in Consumer Culture10 Jan 2022
- 2021Efficient Multi-Modal Embeddings from Structured Data6 Oct 2021
- 2021A comparative evaluation and analysis of three generations of Distributional Semantic Models20 May 2021
- 2021Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation8 Apr 2021