Work on probabilistic models of natural language tends to focus on strings and trees, but there is increasing interest in more general graph-shaped structures since they seem to be better suited for representing natural language semantics, ontologies, or other varieties of knowledge structures. However, while there are relatively simple approaches to defining generative models over strings and trees, it has proven more challenging for more general graphs. This paper describes a natural generalization of the n-gram to graphs, making use of Hyperedge Replacement Grammars to define generative models of graph languages.
|Title of host publication||FSMNLP 2013|
|Subtitle of host publication||Proceedings of the 11th International Conference on Finite State Methods and Natural Language Processing|
|Place of Publication||Stroudsburg, PA|
|Publisher||Association for Computational Linguistics|
|Number of pages||9|
|Publication status||Published - 2013|
|Event||International Conference on Finite State Methods and Natural Language Processing (11th : 2013) - St Andrews, Scotland|
Duration: 15 Jul 2013 → 17 Jul 2013
|Conference||International Conference on Finite State Methods and Natural Language Processing (11th : 2013)|
|City||St Andrews, Scotland|
|Period||15/07/13 → 17/07/13|
Jones, B. K., Goldwater, S., & Johnson, M. (2013). Modeling graph languages with grammars extracted via tree decompositions. In FSMNLP 2013: Proceedings of the 11th International Conference on Finite State Methods and Natural Language Processing (pp. 54-62). Stroudsburg, PA: Association for Computational Linguistics.