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


New from Oxford University Press!

ad

Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


New from Cambridge University Press!

ad

Thesaurus of English Words and Phrases

By Peter Mark Roget

This book "supplies a vocabulary of English words and idiomatic phrases 'arranged … according to the ideas which they express'. The thesaurus, continually expanded and updated, has always remained in print, but this reissued first edition shows the impressive breadth of Roget's own knowledge and interests."


New from Brill!

ad

The Brill Dictionary of Ancient Greek

By Franco Montanari

Coming soon: The Brill Dictionary of Ancient Greek by Franco Montanari is the most comprehensive dictionary for Ancient Greek to English for the 21st Century. Order your copy now!


Academic Paper


Title: Using automatically labelled examples to classify rhetorical relations: an assessment
Author: Caroline Sporleder
Institution: Universiteit van Tilburg
Author: Alex Lascarides
Homepage: http://www.cogsci.ed.ac.uk/~alex/
Institution: University of Edinburgh
Linguistic Field: Computational Linguistics; Discourse Analysis
Abstract: Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples).

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 14, 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