The proliferation of mobile devices, along with advances in speech and natural language processing technologies, have given birth to a new wave of personal assistance applications that enable users to quickly and more naturally perform many tasks through voice on their smart devices. This paper focuses on a natural language understanding (NLU) solution for one such application. We adopted a data-driven approach, aiming to take advantage of large volume of deployment data for continued learning and system improvement. In this paper, we compare two different statistical models . a hidden Markov model and a maximum entropy Markov model . for the task of semantic slot extraction, and we present empirical results on real user data.
Bibliographic reference. Liu, Ding / Cheung, Anthea / Margolis, Anna / Redmond, Patrick / Suh, Jun-won / Wang, Chao (2013): "Data driven methods for utterance semantic tagging", In INTERSPEECH-2013, 2068-2070.