Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.

Description

This paper presents the results of a large-scale evaluation study of window-based Distributional Semantic Models on a wide variety of tasks. Our study combines a broad coverage of model parameters with a model selection methodology that is robust to overfitting and able to capture parameter interactions. We show that our strategy allows us to identify parameter configurations that achieve good performance across different datasets and tasks.

Questions and Answers

You need to be logged in to be able to post here.