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The Effects of Data Size and Frequency Range on Distributional Semantic Models
September 27, 2016 · Declared Dead · 🏛 Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Magnus Sahlgren, Alessandro Lenci
arXiv ID
1609.08293
Category
cs.CL: Computation & Language
Citations
65
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
2 months ago
Abstract
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.
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