Hyperedge replacement grammar (HRG) is a formalism for generating and transforming graphs that has potential applications in natural language understanding and generation. A recognition algorithm due to Lautemann is known to be polynomial-time for graphs that are connected and of bounded degree. We present a more precise characterization of the algorithm's complexity, an optimization analogous to binarization of contextfree grammars, and some important implementation details, resulting in an algorithm that is practical for natural-language applications. The algorithm is part of Bolinas, a new software toolkit for HRG processing.9 page(s
A key problem in semantic parsing with graph-based semantic representations is graph parsing, i.e. c...
This paper is intended as a step towards a theoretically sound approach to learning from graphs, by ...
Graph parsing is known to be computationally expensive. For this reason the construction of special-...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
AbstractWe introduce a hypergraph-generating system, called HRNCE grammars, which is structurally si...
With the abundance of large sets of relational data, methods for analyzing and providing a compact r...
With the abundance of large sets of relational data, methods for analyzing and providing a compact r...
Two types of hypergraph rewriting grammar are considered: the well-known context-free hypergraph gra...
A key problem in semantic parsing with graph-based semantic representations is graph parsing, i.e. c...
This paper is intended as a step towards a theoretically sound approach to learning from graphs, by ...
Graph parsing is known to be computationally expensive. For this reason the construction of special-...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even u...
AbstractWe introduce a hypergraph-generating system, called HRNCE grammars, which is structurally si...
With the abundance of large sets of relational data, methods for analyzing and providing a compact r...
With the abundance of large sets of relational data, methods for analyzing and providing a compact r...
Two types of hypergraph rewriting grammar are considered: the well-known context-free hypergraph gra...
A key problem in semantic parsing with graph-based semantic representations is graph parsing, i.e. c...
This paper is intended as a step towards a theoretically sound approach to learning from graphs, by ...
Graph parsing is known to be computationally expensive. For this reason the construction of special-...