Paper Info
Title
Extraction of templates from phrases using Sequence Binary Decision Diagrams.
Abstract
The extraction of templates such as regard X as Y' from a set of related phrases requires the identification of their internal structures. This paper presents an unsupervised approach for extracting templates on-the-fly from only tagged text by using a novel relaxed variant of the Sequence Binary Decision Diagram (SeqBDD). A SeqBDD can compress a set of sequences into a graphical structure equivalent to a minimal deterministic finite state automata, but more compact and better suited to the task of template extraction. The main contribution of this paper is a relaxed form of the SeqBDD construction algorithm that enables it to form general representations from a small amount of data. The process of compression of shared structures in the text during Relaxed SeqBDD construction, naturally induces the templates we wish to extract. Experiments show that the method is capable of high-quality extraction on tasks based on verb+preposition templates from corpora and phrasal templates from short messages from social media.
Year
DOI
Venue
2018
10.1017/S1351324918000268
NATURAL LANGUAGE ENGINEERING
Field
DocType
Volume
Verb,Computer science,Binary decision diagram,Finite-state machine,Natural language processing,Artificial intelligence,Template,Fold (higher-order function)
Journal
24
Issue
ISSN
Citations 
5.0
1351-3249
0
PageRank 
References 
Authors
0.34
2
3
Name
Order
Citations
PageRank
D. Hirano100.34
Kumiko Tanaka-Ishii226136.69
Andrew M. Finch318521.44