Machine Learning
Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Semantic composition with (robust) minimal recursion semantics
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
A general, abstract model of incremental dialogue processing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Jointly identifying predicates, arguments and senses using Markov logic
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A simple method for resolution of definite reference in a shared visual context
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Learning context-dependent mappings from sentences to logical form
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Comparing local and sequential models for statistical incremental natural language understanding
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Learning dependency-based compositional semantics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Web information extraction using markov logic networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Situated incremental natural language understanding using Markov Logic Networks
Computer Speech and Language
Hi-index | 0.00 |
We present work on understanding natural language in a situated domain, that is, language that possibly refers to visually present entities, in an incremental, word-by-word fashion. Such type of understanding is required in conversational systems that need to act immediately on language input, such as multi-modal systems or dialogue systems for robots. We explore a set of models specified as Markov Logic Networks, and show that a model that has access to information about the visual context of an utterance, its discourse context, as well as the linguistic structure of the utterance performs best. We explore its incremental properties, and also its use in a joint parsing and understanding module. We conclude that mlns offer a promising framework for specifying such models in a general, possibly domain-independent way.