54 research outputs found
A Data-Oriented Approach to Semantic Interpretation
In Data-Oriented Parsing (DOP), an annotated language corpus is used as a
stochastic grammar. The most probable analysis of a new input sentence is
constructed by combining sub-analyses from the corpus in the most probable way.
This approach has been succesfully used for syntactic analysis, using corpora
with syntactic annotations such as the Penn Treebank. If a corpus with
semantically annotated sentences is used, the same approach can also generate
the most probable semantic interpretation of an input sentence. The present
paper explains this semantic interpretation method, and summarizes the results
of a preliminary experiment. Semantic annotations were added to the syntactic
annotations of most of the sentences of the ATIS corpus. A data-oriented
semantic interpretation algorithm was succesfully tested on this semantically
enriched corpus.Comment: 10 pages, Postscript; to appear in Proceedings Workshop on
Corpus-Oriented Semantic Analysis, ECAI-96, Budapes
Bracketing Input for Accurate Parsing
PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200
A New Statistical Parser Based on Bigram Lexical Dependencies
This paper describes a new statistical parser which is based on probabilities
of dependencies between head-words in the parse tree. Standard bigram
probability estimation techniques are extended to calculate probabilities of
dependencies between pairs of words. Tests using Wall Street Journal data show
that the method performs at least as well as SPATTER (Magerman 95, Jelinek et
al 94), which has the best published results for a statistical parser on this
task. The simplicity of the approach means the model trains on 40,000 sentences
in under 15 minutes. With a beam search strategy parsing speed can be improved
to over 200 sentences a minute with negligible loss in accuracy.Comment: 8 pages, to appear in Proceedings of ACL 96. Uuencoded gz-compressed
postscript file created by csh script uufile
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