Making large-scale support vector machine learning practical
Advances in kernel methods
Probabilistic question answering on the web
Proceedings of the 11th international conference on World Wide Web
The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics S.)
The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics S.)
Learning question classifiers: the role of semantic information
Natural Language Engineering
Dealing with Spoken Requests in a Multimodal Question Answering System
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
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This paper investigates the potentialities of a lightweight approach to the Expected Answer Type (EAT) recognition task in a specific restricted-domain Question Answering scenario. In such scenario, the input is represented by automatically transcribed spoken requests, possibly affected by transcription errors. Our objective is to demonstrate that, when dealing with sub-optimal (i.e. noisy) inputs, good performance can be easily achieved with a Machine Learning approach based on simple features extracted from unprocessed questions. In contrast to traditional approaches dealing with questions pre-processed at different levels (including lemmatization, part of speech (POS) tagging, and multiword recognition), the advantage of our lightweight approach is that extra errors often derived from processing noisy data are avoided.