Idiomaticity Prediction of Chinese Noun Compounds and Its Applications

Idiomaticity refers to the situation where the meaning of a lexical unit cannot be derived from the usual meanings of its constituents. As a ubiquitous phenomenon in languages, the existence of idioms often causes significant challenges for semantic NLP tasks. While previous research mostly focuses on the idiomatic usage detection of English verb-noun combinations and the semantic analysis of Noun Compounds (NCs), the idiomaticity issues of Chinese NCs have been rarely studied. In this work, we aim at classifying Chinese NCs into four idiomaticity degrees. Each idiomaticity degree refers to a specific paradigm of how the NCs should be interpreted. To address this task, a Relational and Compositional Representation Learning model (RCRL) is proposed, which considers the relational textual patterns and the compositionality levels of Chinese NCs. RCRL learns relational representations of NCs to capture the semantic relations between two nouns within an NC, expressed by textual patterns and their statistical signals in the corpus. It further employs compositional representations to model the compositionality levels of NCs via network embeddings. Both loss functions of idiomaticity degree classification and representation learning are jointly optimized in an integrated neural network. Experiments over two datasets illustrate the effectiveness of RCRL, outperforming state-of-the-art approaches. Three applicational studies are further conducted to show the usefulness of RCRL and the roles of idiomaticity prediction of Chinese NCs in the fields of NLP.