Request Correction

Towards a Standardized Dataset for Noun Compound Interpretation

Noun compounds are interesting constructs in Natural Language Processing (NLP). Interpretation of noun compounds is the task of uncovering a relationship between component nouns of a noun compound. There has not been much progress in this field due to lack of a standardized set of relation inventory and associated annotated dataset which can be used to evaluate suggested solutions. Available datasets in the literature suffer from two problems. Firstly, the approaches to creating some of the relation inventories and datasets are statistically motivated, rather than being linguistically motivated. Secondly, there is little overlap among the semantic relation inventories used by them. We attempt to bridge this gap through our paper. We present a dataset that is (a) linguistically grounded by using Levi (1978)'s theory, and (b) uses frame elements of FrameNet as its semantic relation inventory. The dataset consists of 2,600 examples created by an automated extraction from FrameNet annotated corpus, followed by a manual investigation. These attributes make our dataset useful for noun compound interpretation in a general-purpose setting.