Machine Learning Research Higlights

VIDI Ivan Titov: Scaling Semantic Parsing to Unrestricted Domains

NWO awarded Ivan Titov (UvA) a VIDI grant for the proposal “Scaling Semantic Parsing to Unrestricted Domains”.

Enabling a machine to understand human language – that is, to process any input text or utterance and be able to answer questions or perform actions on its basis – is one of the main goals of natural language processing. The lack of accurate methods for predicting meaning representations of texts is the key bottleneck for many natural language processing applications such as question answering, text summarization and information retrieval. Although state-of-the-art semantic parsers work fairly well on closed domains (e.g., interpreting natural language queries to databases), accurately predicting even shallow forms of semantic representations for less restricted texts remains a challenge. The reason for the unsatisfactory performance is reliance on supervised learning from human-annotated text collections, with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. In order to deal with this challenge, in this project, we will introduce methods for inducing semantic parsers primarily from un-annotated data (e.g., text on the Web). More here.