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Interpreting compound nouns with kernel methods

Published online by Cambridge University Press:  12 March 2013

DIARMUID Ó SÉAGHDHA  and
ANN COPESTAKE
DIARMUID Ó SÉAGHDHA
Affiliation:
Computer Laboratory, University of Cambridge, Cambridge, UK e-mail: do242@cam.ac.uk, ann.copestake@cl.cam.ac.uk
ANN COPESTAKE
Affiliation:
Computer Laboratory, University of Cambridge, Cambridge, UK e-mail: do242@cam.ac.uk, ann.copestake@cl.cam.ac.uk
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Abstract

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.

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Type
Articles
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Natural Language Engineering , Volume 19 , Special Issue 3: On the semantics of noun compounds , July 2013 , pp. 331 - 356
Copyright
Copyright © Cambridge University Press 2013 

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