Abstract / Description of output
When humans speak they often use grammatically
incorrect sentences, which is a
problem for grammar-based language processing
methods, since they expect input
that is valid for the grammar. We
present two methods to transform spoken
language into grammatically correct sentences.
The first is an algorithm for automatic
ellipsis detection, which finds ellipses
in spoken sentences and searches
in a combinatory categorial grammar for
suitable words to fill the ellipses. The second
method is an algorithm that computes
the semantic similarity of two words using
WordNet, which we use to find alternatives
to words that are unknown to the
grammar. In an evaluation, we show that
the usage of these two methods leads to
an increase of 38.64% more parseable sentences
on a test set of spoken sentences
that were collected during a human-robot
interaction experiment.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL) |
| Place of Publication | Philadelphia, PA, U.S.A. |
| Publisher | Association for Computational Linguistics |
| Pages | 243-250 |
| Number of pages | 8 |
| Publication status | Published - 1 Jun 2014 |