Determining the Compositionality of Noun-Adjective Pairs with Lexical Variants and Distributional Semantics
Authors: Marco S.G. Senaldi, Gianluca E. Lebani, Alessandro Lenci (Scuola Normale Superiore, Università di Pisa, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this work we employed a set of 26 Italian noun-adjective expressions to test compositionality indices that compare the distributional vector of an expression with the vectors of its lexical variants. These were obtained by replacing the components of the original expression with semantically related words. Our indices performed comparably or better than other compositionality measures reported in the distributional literature.

Introduction
While a white car is white and is a car, a red herring in a sentence like I thought he was the culprit, but he was a red herring is neither red nor a herring, but indicates something that distracts someone from a relevant issue. The former expression is compositional, since its meaning derives from the composition of the meanings of its subparts AUTHOR. The latter, by contrast, is an idiom, a non-compositional, figurative and proverbial word combination belonging to the wider class of Multiword Expressions AUTHOR. The compositionality of a given expression entails salva-veritate-interchangeability and systematicity AUTHOR. First of all, if we replace the constituents of a compositional expression with synonyms or similar words (e.g., from white car to white automobile), the whole meaning is not altered. Secondly, if we can understand the meaning of white car and red herring used in the literal sense, we can also understand what white herring and red car mean. Both these properties are not valid for idioms, which always exhibit lexical fixedness to some extent: variants of idiomatic red herring like red fish or white herring can just have a literal reading. Computational studies to date have proposed several techniques to automatically measure idiomaticity. Of note, AUTHOR and AUTHOR label a given word combination as idiomatic if the Pointwise Mutual Information (PMI) AUTHOR between its component words is higher than the PMIs between the components of a set of lexical variants of this combination. These variants are obtained by replacing the component words of the original expressions with semantically related words. Other researches have exploited Distributional Semantic Models (DSMs) AUTHOR, comparing the vector of a given phrase with the single vectors of its subparts AUTHOR or comparing the vector of a phrase with the vector deriving from the sum or the products of their components AUTHOR. In a previous contribution AUTHOR, we started from a set of Italian verbal idiomatic and non-idiomatic phrases (henceforth our targets) and generated lexical variants (simply variants henceforth) by replacing their components with semantic neighbours extracted from a linear DSM and Italian MultiWordNet AUTHOR. Then, instead of measuring the associational scores between their subparts like in AUTHOR and AUTHOR, we exploited Distributional Semantics to observe how different the context vectors of our targets were from the vectors of their variants. Our proposal stemmed from the consideration that a high PMI value does not necessarily imply the idiomatic or multiword status of an expression, but just that its components co-occur more frequently than expected by chance, as in the case of read and book or solve and problem, which are all instances of compositional pairings. By contrast, what watertightly distinguishes an idiomatic from a collocation-like yet still compositional expression is their context of use. Comparing the distributional contexts of the original expressions and their alternatives should therefore represent a more precise refinement of the PMI-based procedure. Actually, idiomatic expressions vectors were found to be less similar to their variants vectors with respect to compositional expressions vectors. In some of our models, we also kept track of the variants that were not attested in our corpus by representing them as orthogonal vectors to the vector of the original expression, still achieving considerable results. Noteworthily, most researches conducted so far have focused on verbal idioms, while the analysis of NP idioms like red herring or second thoughts has been usually left aside.

Conclusion
In this contribution we applied to adjective-noun constructions the variant-based distributional measures we had previously tested on verbal idioms AUTHOR, obtaining effective performances. Interestingly, our measures performed comparably to or even better than the Additive method proposed in the distributional literature AUTHOR, while the Multiplicative one performed considerably worst than all our models, together with the Random baseline. Future work will concern testing whether these variant-based measures can be succesfully exploited to predict psycholinguistic data about the processing of idiom compositionality and flexibility, together with other corpus-based indices of idiomaticity.