Computer Science – Computation and Language
Scientific paper
1996-09-09
Journal of Artificial Intelligence Research 5 (1996) 53-94
Computer Science
Computation and Language
42 pages, uses jair.sty, theapa.bst, theapa.sty
Scientific paper
Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.
No associations
LandOfFree
Cue Phrase Classification Using Machine Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Cue Phrase Classification Using Machine Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cue Phrase Classification Using Machine Learning will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-335672