Machine learning helps computers predict near-synonyms

Choosing the best word or phrase for a given context from among candidate near-synonyms, such as “slim” and “skinny”, is something that human writers, given some experience, do naturally; but for choices with this level of granularity, it can be a difficult selection problem for computers.

Researchers from Macquarie University in Australia have published an article in the journal Natural Language Engineering, investigating whether they could use machine learning to re-predict a particular choice among near-synonyms made by a human author – a task known as the lexical gap problem.

They used a supervised machine learning approach to this problem in which the weights of different features of a document are learned computationally. Through using this approach, the computers were able to predict . . . → Read More: Machine learning helps computers predict near-synonyms