9,357 research outputs found
Semanticizing syntactic patterns in NLP processing using SPARQL-DL queries
Some recent works on natural language semantic parsing make use of syntax and semantics together using different combination models. In our work we attempt to use SPARQL-DL as an interface between syntactic information given by the Stanford statistical parser (namely part-of-speech tagged text and typed dependency representation) and semantic information obtained from the FrameNet database. We use SPARQL-DL queries to check the presence of syntactic patterns within a sentence and identify their role as frame elements. The choice of SPARQL-DL is due to its usage as a common reference language for semantic applications and its high expressivity, which let rules to be generalized exploiting the inference capabilities of the underlying reasoner
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
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