Error-Aware Probabilistic Parsing
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If you have a question about this talk, please contact Johanna Geiss.
Given the human propensity to err, a parser must be able to produce
accurate analyses for sentences which are deviant according to human
standards, yet which we routinely interpret correctly. State-of-the-
art probabilistic parsers are generally robust to errors, and they
will return analyses for most ungrammatical sentences. However, these
robust analyses are not necessarily correct because they do not always
reflect the meanings of the ill-formed sentences. I present a two-
stage “error-aware” probabilistic parsing architecture which uses two
versions of a probabilistic parser, one trained on a normal treebank and
the other trained on an automatically created ungrammatical version of the
original treebank. A binary classifier is used to decide which version to
employ. I present the results of experiments carried out using the
“error-aware” probabilistic parsing architecture and the Penn Treebank. I
also
present the results of experiments carried out with various
grammatical/ungrammatical classifiers and the BNC .
This talk is part of the NLIP Seminar Series series.
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