This paper investigates the effects of data size and frequency range on distributional seman- tic 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 re- sults 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 fac- torized model
The Effects of Data Size and Frequency Range on Distributional Semantic Models
LENCI, ALESSANDROCo-primo
2016-01-01
Abstract
This paper investigates the effects of data size and frequency range on distributional seman- tic 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 re- sults 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 fac- torized modelFile in questo prodotto:
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