text: The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincare embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincare similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincare embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.
  @cite_26: In this paper we explore the use of selectional preferences for detecting noncompositional verb-object combinations. To characterise the arguments in a given grammatical relationship we experiment with three models of selectional preference. Two use WordNet and one uses the entries from a distributional thesaurus as classes for representation. In previous work on selectional preference acquisition, the classes used for representation are selected according to the coverage of argument tokens rather than being selected according to the coverage of argument types. In our distributional thesaurus models and one of the methods using WordNet we select classes for representing the preferences by virtue of the number of argument types that they cover, and then only tokens under these classes which are representative of the argument head data are used to estimate the probability distribution for the selectional preference model. We demonstrate a highly signicant correlation between measures which use these ‘typebased’ selectional preferences and compositionality judgements from a data set used in previous research. The type-based models perform better than the models which use tokens for selecting the classes. Furthermore, the models which use the automatically acquired thesaurus entries produced the best results. The correlation for the thesaurus models is stronger than any of the individual features used in previous research on the same dataset.
  @cite_13: Much work on idioms has focused on type identification, i.e., determining whether a sequence of words can form an idiomatic expression. Since an idiom type often has a literal interpretation as well, token classification of potential idioms in context is critical for NLP. We explore the use of informative prior knowledge about the overall syntactic behaviour of a potentially-idiomatic expression (type-based knowledge) to determine whether an instance of the expression is used idiomatically or literally (token-based knowledge). We develop unsupervised methods for the task, and show that their performance is comparable to that of state-of-the-art supervised techniques.
summary: Some of the initial efforts on compositionality prediction were undertaken by , who use LSA to calculate the similarity between a phrase and its components, whereas extend this idea with collocation features (e.g., phrase frequency, point-wise mutual information). Researchers also tried to identify non-compositionality in verb-noun phrases using syntax @cite_13 and selectional preferences @cite_26 . Attempts to examine the possibility to derive the semantics of a compound or multiword expression from its parts have been researched extensively . define a compositionality score and use different vector operations to estimate the semantic distance between a phrase and its individual components. Some of the investigations are made for compositionality detection using representation learning of word embeddings . also show that distributional similarity over multiple languages can help in improving the quality of compositionality prediction.