13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

Exploiting the Semantic Web for Unsupervised Natural Language Semantic Parsing

Gokhan Tur, Minwoo Jeong, Ye-Yi Wang, Dilek Hakkani-Tür, Larry Heck

Microsoft, Mountain View, CA, USA

In this paper, we propose to bring together the semantic web experience and statistical natural language semantic parsing modeling. The idea is that, the process for populating knowledge-bases by semantically parsing structured web pages may provide very valuable implicit annotation for language understanding tasks. We mine search queries hitting to these web pages in order to semantically annotate them for building statistical unsupervised slot filling models, without even a need for a semantic annotation guideline. We present promising results demonstrating this idea for building an unsupervised slot filling model for the movies domain with some representative slots. Furthermore, we also employ unsupervised model adaptation for cases when there are some in-domain unannotated sentences available. Another key contribution of this work is using implicitly annotated natural-language-like queries for testing the performance of the models, in a totally unsupervised fashion. We believe, such an approach also ensures consistent semantic representation between the semantic parser and the backend knowledge-base.

Index Terms: semantic parsing, semantic web, semantic search, dialog, natural language understanding

Full Paper

Bibliographic reference.  Tur, Gokhan / Jeong, Minwoo / Wang, Ye-Yi / Hakkani-Tür, Dilek / Heck, Larry (2012): "Exploiting the semantic web for unsupervised natural language semantic parsing", In INTERSPEECH-2012, 338-341.