Weighted DAG Automata for Semantic Graphs

David Chiang, Frank Drewes, Daniel Gildea, Adam Lopez, Giorgio Satta

Abstract
Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that parallels the frameworks of finite automata for strings and finite tree automata for trees. A possible starting point for such a framework is the formalism of directed acyclic graph (DAG) automata, defined by Kamimura and Slutzki and extended by Quernheim and Knight. In this article, we study the latter in depth, demonstrating several new results, including a practical recognition algorithm that can be used for inference and learning with models defined on DAG automata. We also propose an extension to graphs with unbounded node degree and show that our results carry over to the extended formalism.
Anthology ID:
J18-1005
Volume:
Computational Linguistics, Volume 44, Issue 1 - April 2018
Month:
April
Year:
2018
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
119–186
Language:
URL:
https://aclanthology.org/J18-1005
DOI:
10.1162/COLI_a_00309
Bibkey:
Cite (ACL):
David Chiang, Frank Drewes, Daniel Gildea, Adam Lopez, and Giorgio Satta. 2018. Weighted DAG Automata for Semantic Graphs. Computational Linguistics, 44(1):119–186.
Cite (Informal):
Weighted DAG Automata for Semantic Graphs (Chiang et al., CL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-xml-comments/J18-1005.pdf
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