Paper Info
Title
Using automatically labelled examples to classify rhetorical relations: An assessment
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).
Year
DOI
Venue
2008
10.1017/S1351324906004451
Natural Language Engineering
Keywords
Field
DocType
corresponding relation,suitable training material,rhetorical relation,labelled data,labelled training data,labelled example,unmarked data,lexically marked example,discourse marker,automatic labelling process,unmarked example,machine learning,mean shift,natural language processing
Training set,Discourse relation,Computer science,Rhetorical question,Artificial intelligence,Natural language processing,Classifier (linguistics),Discourse marker
Journal
Volume
Issue
Citations 
14
3
62
PageRank 
References 
Authors
3.30
24
2
Name
Order
Citations
PageRank
Caroline Sporleder145331.84
alex lascarides250368.41