Publishing Partner: Cambridge University Press CUP Extra Wiley-Blackwell Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

The Social Origins of Language

By Daniel Dor

Presents a new theoretical framework for the origins of human language and sets key issues in language evolution in their wider context within biological and cultural evolution


New from Cambridge University Press!

ad

Preposition Placement in English: A Usage-Based Approach

By Thomas Hoffmann

This is the first study that empirically investigates preposition placement across all clause types. The study compares first-language (British English) and second-language (Kenyan English) data and will therefore appeal to readers interested in world Englishes. Over 100 authentic corpus examples are discussed in the text, which will appeal to those who want to see 'real data'


New from Brill!

ad

Free Access 4 You

Free access to several Brill linguistics journals, such as Journal of Jewish Languages, Language Dynamics and Change, and Brill’s Annual of Afroasiatic Languages and Linguistics.


Academic Paper


Title: Interpreting compound nouns with kernel methods
Author: Diarmuid Ó Séaghdha
Institution: Computer Laboratory, University of Cambridge, UK
Author: Ann Copestake
Email: click here to access email
Homepage: http://www-csli.stanford.edu/~aac/
Institution: Stanford University
Linguistic Field: Computational Linguistics
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.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 19, Issue 3, which you can read on Cambridge's site or on LINGUIST .



Back
Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page