Olga Mudraya

Measuring MWE Compositionality Using Semantic Annotation

This paper reports on an experiment in which we explore a new approach to the automatic measurement of multi-word expression (MWE) compositionality. We propose an algorithm which ranks MWEs by their compositionality relative to a semantic field taxonomy based on the Lancaster English semantic lexicon (Piao et al., 2005a). The semantic information provided by the lexicon is used for measuring the semantic distance between a MWE and its constituent words. The algorithm is evaluated both on 89 manually ranked MWEs and on McCarthy et als (2003) manually ranked phrasal verbs. We compared the output of our tool with human judgments using Spearmans rank-order correlation coefficient. Our evaluation shows that the automatic ranking of the majority of our test data (86.52%) has strong to moderate correlation with the manual ranking while wide discrepancy is found for a small number of MWEs. Our algorithm also obtained a correlation of 0.3544 with manual ranking on McCarthy et als test data, which is comparable or better than most of the measures they tested. This experiment demonstrates that a semantic lexicon can assist in MWE compositionality measurement in addition to statistical algorithms.

ASSIST: automated semantic assistance for translators

The problem we address in this paper is that of providing contextual examples of translation equivalents for words from the general lexicon using comparable corpora and semantic annotation that is uniform for the source and target languages. For a sentence, phrase or a query expression in the source language the tool detects the semantic type of the situation in question and gives examples of similar contexts from the target language corpus.