Comparing morphological and syntactic variations of support verb constructions and verbal full phrasemes in French: a corpus based study

International audienceThis paper deals with syntactic and morphological variations of verbal MWEs in French. Our objective was to check against corpus evidence some assumptions of the literature concerning MWEs, and more precisely verbal MWEs, which are often said to be quite variable (e.g. Nunberg et al. 1994; Moon 1998). We wanted to check to what extent this claim was proved to be true in a corpus study for 30 frequent verbal MWEs in French, by comparing the syntactic and morpho-logical variations between non compositional MWEs and verbal collocations, particularly support verb constructions (hereafter SVCs), which are very frequent in French

CCG Parsing and Multiword Expressions

This thesis presents a study about the integration of information about Multiword Expressions (MWEs) into parsing with Combinatory Categorial Grammar (CCG). We build on previous work which has shown the benefit of adding information about MWEs to syntactic parsing by implementing a similar pipeline with CCG parsing. More specifically, we collapse MWEs to one token in training and test data in CCGbank, a corpus which contains sentences annotated with CCG derivations. Our collapsing algorithm however can only deal with MWEs when they form a constituent in the data which is one of the limitations of our approach. We study the effect of collapsing training and test data. A parsing effect can be obtained if collapsed data help the parser in its decisions and a training effect can be obtained if training on the collapsed data improves results. We also collapse the gold standard and show that our model significantly outperforms the baseline model on our gold standard, which indicates that there is a training effect. We show that the baseline model performs significantly better on our gold standard when the data are collapsed before parsing than when the data are collapsed after parsing which indicates that there is a parsing effect. We show that these results can lead to improved performance on the non-collapsed standard benchmark although we fail to show that it does so significantly. We conclude that despite the limited settings, there are noticeable improvements from using MWEs in parsing. We discuss ways in which the incorporation of MWEs into parsing can be improved and hypothesize that this will lead to more substantial results. We finally show that turning the MWE recognition part of the pipeline into an experimental part is a useful thing to do as we obtain different results with different recognizers.Comment: MSc thesis, The University of Edinburgh, 2014, School of Informatics, MSc Artificial Intelligenc