Freebase Semantic Parsing With And Without QA Pairs
I will present three semantic parsing approaches for querying Freebase in natural language 1) training only on raw web corpus, 2) training on question-answer (QA) pairs and 3) training on both QA pairs and web corpus. For 1 and 2, we conceptualise semantic parsing as a graph matching problem, where natural language graphs built using CCG/dependency logical forms are transduced to Freebase graphs. For 3, I will present a natural-logic approach for SemanticParsing. Our methods achieve state-of-the-art on WebQuestions and Free917 QA datasets.
Siva Reddy is a Google PhD fellow at the University of Edinburgh under the supervision of Mirella Lapata and Mark Steedman. His primary research interests are in semantic parsing, information extraction, distributional semantics and cross-language transfer. His work is published in TACL, ACL, NAACL, EMNLP. He won the best paper award in IJCNLP 2011, a first place in SemEval 2011 Compositionality Detection task, and a second place in SemEval 2010 WSD task. He worked with Google Parsing team as an intern during his PhD, and as a full-time employee for Adam Kilgarriff’s Sketch Engine before starting his PhD. Apart from language, he loves Badminton (represents Edinburgh University in league matches), and is also learning to play Irish whistle. He is currently on the job market looking for a postdoc.