We present a tagged corpus for English noun compound interpretation and describe the method used to generate them. In order to collect noun compounds, we extracted binary noun compounds (i.e. noun-noun pairs) by looking for sequences of two nouns in the POS tag data of the Wall Street Journal. We then manually filtered out all noun compounds which were incorrectly tagged or included proper noun...

We present a new, sizeable dataset of noun– noun compounds with their syntactic analysis (bracketing) and semantic relations. Derived from several established linguistic resources, such as the Penn Treebank, our dataset enables experimenting with new approaches towards a holistic analysis of noun–noun compounds, such as jointlearning of noun–noun compounds bracketing and interpretation, as well...

There are several theories regarding what influences prominence assignment in English noun-noun compounds. We developed corpus-driven models for automatically predicting prominence assignment in noun-noun compounds using feature sets based on two such theories: the informativeness theory and the semantic composition theory. The evaluation of the prediction models indicate that though both of th...

Can relations described by English nounnoun compounds be adequately captured by prepositions? We attempt to answer this question in a data-driven way, using gamification to annotate a set of about a thousand noun-noun compound examples. Annotators could make a choice out of five prepositions generated with the help of paraphrases found in the Google ngram corpus. We show that there is substanti...

We present a novel, simple, unsupervised method for characterizing the semantic relations that hold between nouns in noun-noun compounds. The main idea is to discover predicates that make explicit the hidden relations between the nouns. This is accomplished by writing Web search engine queries that restate the noun compound as a relative clause containing a wildcard character to be filled in wi...