NDLI: Learning to rank for why-question answering

Content Provider	Springer Nature Link
Author	Verberne, Suzan Halteren, Hans Theijssen, Daphne Raaijmakers, Stephan Boves, Lou
Copyright Year	2010
Abstract	In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM $^{map}$) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval.
Starting Page	107
Ending Page	132
Page Count	26
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	14
Issue Number	2
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-06-05
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Learning to rank Question answering Why-questions Information Storage and Retrieval Data Structures, Cryptology and Information Theory Pattern Recognition Document Preparation and Text Processing Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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