Which Step Do I Take First? Troubleshooting with Bayesian Models
Author(s) -
Annie Louis,
Mirella Lapata
Publication year - 2015
Publication title -
transactions of the association for computational linguistics
Language(s) - English
Resource type - Journals
ISSN - 2307-387X
DOI - 10.1162/tacl_a_00123
Subject(s) - troubleshooting , computer science , readability , installation , generative grammar , bayesian probability , order (exchange) , online community , generative model , information retrieval , world wide web , machine learning , artificial intelligence , data science , programming language , finance , economics , operating system
Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.
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