Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences

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.