In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. This approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Tree-bank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method. A data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the English ATIS corpus and the Dutch OVIS corpus. Experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.

In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. This approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Tree-bank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method. A data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the English ATIS corpus and the Dutch OVIS corpus. Experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.

This paper presents a memory-based model of human syntactic processing: data-oriented parsing. After a brief introduction (section 1), it argues that any account of syntatic disambiguation inevitably has an important memory-based component (section 2). It discusses the limitations of probabilistically enhanced competence-grammars, and argues for a more principled memory-based approach (section 3). In sections 4 and 5, one particular memory-based model is described in some detail: a simple instantiation of the`data-oriented parsing' approach (`DOP1'). Section 6 reports on experimentally established properties of this model, and section 7 compares it with other memory-based techniques. Section 8 concludes and points to future work.

Link to this article: http://journals.cambridge.org/abstract_S1351324908004956 How to cite this article: MARTIJN SPITTERS, MARCO DE BONI, JAKUB ZAVREL and REMKO BONNEMA (2009). Learning effective and engaging strategies for advice-giving human-machine dialogue. Natural Language Engineering, 15, pp 355-378 AbstractWe describe a system that automatically learns effective and engaging dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feedback. Besides the more usual clarification and verification components of dialogue, this library contains various social elements like greetings, apologies, small talk, relational questions and jokes. We tested the method through an experimental dialogue system that encourages takeup of exercise and shows that the learned dialogue policy performs as well as one built by human experts for this system.