The noun compound – a sequence of nouns which functions as a single noun – is very common in English texts. No language processing system should ignore expressions like steel soup pot cover if it wants to be serious about such high-end applications of computational linguistics as question answering, information extraction, text summarization, machine translation – the list goes on. Processing noun compounds, however, is far from trouble-free. For one thing, they can be bracketed in various ways: is it steel soup, steel pot, or steel cover? Then there are relations inside a compound, annoyingly not signalled by any words: does potcontainsoup or is it for cookingsoup? These and many other research challenges are the subject of this special issue.

We discuss the problem of interpreting noun compounds such as colon cancer tumor suppressor protein, which pose major challenges for the automatic interpretation of English written text. We present an overview of the more general process of compounding and of noun compounds in particular, as well as of their syntax and semantics from both theoretical and computational linguistics viewpoint with an emphasis on the latter. Our main focus is on computational approaches to the syntax and semantics of noun compounds: we describe the problems, present the challenges, and discuss the most important lines of research. We also show how understanding noun compound syntax and semantics could help solve textual entailment problems, which would be potentially useful for a number of NLP applications, and which we believe to be an important direction for future research.

This paper presents a classification-based approach to noun–noun compound interpretation within the statistical learning framework of kernel methods. In this framework, the primary modelling task is to define measures of similarity between data items, formalised as kernel functions. We consider the different sources of information that are useful for understanding compounds and proceed to define kernels that compute similarity between compounds in terms of these sources. In particular, these kernels implement intuitive notions of lexical and relational similarity and can be computed using distributional information extracted from text corpora. We report performance on classification experiments with three semantic relation inventories at different levels of granularity, demonstrating in each case that combining lexical and relational information sources is beneficial and gives better performance than either source taken alone. The data used in our experiments are taken from general English text, but our methods are also applicable to other domains and potentially to other languages where noun–noun compounding is frequent and productive.

Many English noun pairs suggest an almost limitless array of semantic interpretation. A fruit bowl might be described as a bowl for fruit, a bowl that contains fruit, a bowl for holding fruit, or even (perhaps in a modern sculpture class), a bowl made out of fruit. These interpretations vary in syntax, semantic denotation, plausibility, and level of semantic detail. For example, a headache pill is usually a pill for preventing headaches, but might, perhaps in the context of a list of side effects, be a pill that can cause headaches (Levi, J. N. 1978. The Syntax and Semantics of Complex Nominals. New York: Academic Press.). In addition to lexical ambiguity, both relational ambiguity and relational vagueness make automatic semantic interpretation of these combinations difficult. While humans parse these possibilities with ease, computational systems are only recently gaining the ability to deal with the complexity of lexical expressions of semantic relations. In this paper, we describe techniques for paraphrasing the semantic relations that can hold between nouns in a noun compound, using a semi-supervised probabilistic method to rank candidate paraphrases of semantic relations, and describing a new method for selecting plausible relational paraphrases at arbitrary levels of semantic specification. These methods are motivated by the observation that existing semantic relation classification schemes often exhibit a highly skewed class distribution, and that lexical paraphrases of semantic relations vary widely in semantic precision.

This paper presents a study on the interpretation and bracketing of noun compounds (‘NCs’) based on lexical semantics. Our primary goal is to develop a method to automatically interpret NCs through the use of semantic relations. Our NC interpretation method is based on lexical similarity with tagged NCs, based on lexical similarity measures derived from WordNet. We apply the interpretation method to both two- and three-term NC interpretation based on semantic roles. Finally, we demonstrate that our NC interpretation method can boost the coverage and accuracy of NC bracketing.

Nominations are now open for the 2013 Annual Karen Spärck Jones Award – awarded to outstanding researchers in Natural Language Processing and Information Retrieval who have been awarded their PhD in the past ten years. The panel especially welcomes nominations for researchers who have a significant experimental component in their work.