A Framework to Predict the Quality of Answers with NonTextual

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.

[1]  Donna K. Harman,et al.  Overview of the First Text REtrieval Conference (TREC-1) , 1992, TREC.

[2]  Jenq-Neng Hwang,et al.  Nonparametric multivariate density estimation: a comparative study , 1994, IEEE Trans. Signal Process..

[3]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[4]  Diane M. Strong,et al.  Data quality in context , 1997, CACM.

[5]  Kristian J. Hammond,et al.  Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System , 1997, AI Mag..

[6]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[7]  Leah S. Larkey,et al.  Automatic essay grading using text categorization techniques , 1998, SIGIR '98.

[8]  Mario Lenz,et al.  Question Answering with Textual CBR , 1998, FQAS.

[9]  W. Bruce Croft,et al.  A language modeling approach to information retrieval , 1998, SIGIR '98.

[10]  M. KleinbergJon.  Authoritative sources in a hyperlinked environment , 1999 .

[11]  Andrew McCallum,et al.  Using Maximum Entropy for Text Classification , 1999 .

[12]  Eriks Sneiders,et al.  Automated FAQ Answering: Continued Experience with Shallow Language Understanding , 1999 .

[13]  Susan Gauch,et al.  Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web , 2000, SIGIR '00.

[14]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[15]  Rob Malouf,et al.  A Comparison of Algorithms for Maximum Entropy Parameter Estimation , 2002, CoNLL.

[16]  Djoerd Hiemstra,et al.  The Importance of Prior Probabilities for Entry Page Search , 2002, SIGIR '02.

[17]  W. Bruce Croft,et al.  Time-based language models , 2003, CIKM '03.

[18]  R. Manmatha,et al.  Using Maximum Entropy for Automatic Image Annotation , 2004, CIVR.

[19]  W. Bruce Croft,et al.  Finding similar questions in large question and answer archives , 2005, CIKM '05.

[20]  Valentin Jijkoun,et al.  Retrieving answers from frequently asked questions pages on the web , 2005, CIKM '05.

[21]  Chung-Hsien Wu,et al.  Domain-specific FAQ retrieval using independent aspects , 2005, TALIP.

[22]  W. Bruce Croft,et al.  Document quality models for web ad hoc retrieval , 2005, CIKM '05.

[23]  Jungyun Seo,et al.  High-performance FAQ retrieval using an automatic clustering method of query logs , 2006, Inf. Process. Manag..

[24]  J. Lafferty,et al.  A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval , 2001, SIGIR Forum.