One of the key challenges in grounded language acquisition is resolving the intentions of the expressions. Typically the task involves identifying a subset of records from a list of candidates as the correct meaning of a sentence. While most current work assume complete or partial independence be- tween the records, we examine a scenario in which they are strongly related. By representing the set of potential meanings as a graph, we explicitly encode the relationships between the candidate meanings. We introduce a refinement algorithm that first learns a lexicon which is then used to remove parts of the graphs that are irrelevant. Experiments in a navigation domain shows that the algorithm successfully recovered over three quarters of the correct semantic content.

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In Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011), June 2011.

@inproceedings{chen.mlslp11, title={Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning}, author={David L. Chen and Raymond J. Mooney}, booktitle={Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011)}, month={June}, url="http://www.cs.utexas.edu/users/ai-lab/?chen:mlslp11", year={2011} }

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