This document discusses deep learning approaches to representation learning of word meanings. It introduces distributional semantic representations, which represent a word based on the frequencies of other words that co-occur with it in a corpus. It also introduces distributed semantic representations, which represent word meanings as low-dimensional dense vectors learned through neural network models like skip-gram. The document provides an example of constructing a distributional representation of the word "apple" based on sentences containing it. It then discusses how these representations can be used for tasks like measuring semantic similarity between words.
This document discusses deep learning approaches to representation learning of word meanings. It introduces distributional semantic representations, which represent a word based on the frequencies of other words that co-occur with it in a corpus. It also introduces distributed semantic representations, which represent word meanings as low-dimensional dense vectors learned through neural network models like skip-gram. The document provides an example of constructing a distributional representation of the word "apple" based on sentences containing it. It then discusses how these representations can be used for tasks like measuring semantic similarity between words.