Semantic parsing

Trends in Semantic Parsing - Part 2

In Part 1 of this two-part series, I discussed some supervised approaches for the objective. In this part, we will look at some unsupervised or semi-supervised approaches, namely a Bayesian model, and transfer learning. An unsupervised Bayesian model This paper was published in ACL 20111, back when statistical methods were still being used for NLP tasks. But with the recent forays into generative models, I feel it has again become relevant to understand how such methods worked.

Trends in Semantic Parsing - Part 1

In this article, I will try to round up some (mostly neural) approaches for semantic parsing and semantic role labeling (SRL). This is not an extensive review of these methods, but just a collection of my notes on reading some recent research on the subject. However, I do believe it covers most of the latest trends as well as their limitations. But first, what is semantic parsing? “Semantic” refers to meaning, and “parsing” means resolving a sentence into its component parts.