Classifying Cue Phrases in Text and Speech Using Machine Learning

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. 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 rules from sets of pre-classified cue phrases and their features. Machine learning is shown to be an effective technique for not only automating the generation of classification rules, but also for improving upon previous results.Comment: 8 pages, PostScript File, to appear in the Proceedings of AAAI-9

Cue Phrase Classification Using Machine Learning

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.Comment: 42 pages, uses jair.sty, theapa.bst, theapa.st

London Congestion Pricing – Implications for Other Cities

Straßenbenutzungsgebühr, Verkehrsstau, Stadtverkehrspolitik, Großbritannien, London, Road pricing, Traffic jam, Urban transport policy, United Kingdom

Congestion costing critique

The Urban Mobility Report (UMR) is a widely-cited study that quantifies and monetises (measures in monetary units) traffic congestion costs in U.S. metropolitan regions. This report critically examines the UMR’s assumptions and methods. The UMR reflects an older planning paradigm which assumes that “transportation” means automobile travel, and so evaluates transport system performance based primarily on automobile travel speeds; it ignores other modes, other planning objectives and other impacts. The UMR methodology overestimates congestion costs and roadway expansion benefits by using higher baseline speeds and travel time unit cost values than most experts recommend, by ignoring induced travel impacts, and using an inaccurate speed-emission curve. Its estimates represent upper-bound values and are two- to four times higher than result from more realistic assumptions. The UMR claims that congestion costs are “massive,” although they increase total travel time and fuel consumption by 2% at most. It exaggerates future congestion problems by ignoring evidence of peaking vehicle travel and changing travel demands. The UMR ignores basic research principles: it fails to identify best current practices, explain assumptions, document sources, incorporate peer review, or respond to criticisms

Guidelines for spaceborne microwave remote sensors

A handbook was developed to provide information and support to the spaceborne remote sensing and frequency management communities: to guide sensor developers in the choice of frequencies; to advise regulators on sensor technology needs and sharing potential; to present sharing analysis models and, through example, methods for determining sensor sharing feasibility; to introduce developers to the regulatory process; to create awareness of proper assignment procedures; to present sensor allocations; and to provide guidelines on the use and limitations of allocated bands. Controlling physical factors and user requirements and the regulatory environment are discussed. Sensor frequency allocation achievable performance and usefulness are reviewed. Procedures for national and international registration, the use of non-allocated bands and steps for obtaining new frequency allocations, and procedures for reporting interference are also discussed