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Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences
BibTeX
@MISC{Giuliani_usingellipsis,
author = {Manuel Giuliani and Thomas Marschall and Amy Isard},
title = {Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences},
year = {}
}
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Abstract
When humans speak they often use gram-matically incorrect sentences, which is a problem for grammar-based language pro-cessing methods, since they expect in-put that is valid for the grammar. We present two methods to transform spoken language into grammatically correct sen-tences. The first is an algorithm for au-tomatic ellipsis detection, which finds el-lipses in spoken sentences and searches in a combinatory categorial grammar for suitable words to fill the ellipses. The sec-ond method is an algorithm that computes the semantic similarity of two words us-ing WordNet, which we use to find alter-natives 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 sen-tences on a test set of spoken sentences that were collected during a human-robot interaction experiment. 1
Keyphrases
spoken language ellipsis detection word similarity grammatically valid sentence spoken sentence suitable word combinatory categorial grammar test set sec-ond method correct sen-tences word us-ing wordnet semantic similarity parseable sen-tences gram-matically incorrect sentence human-robot interaction experiment au-tomatic ellipsis detection grammar-based language pro-cessing method
