Question Routing in Community Question Answering: Putting Category in Its Place 1 The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2 AT&T Labs.

Slides:

Advertisements

Similar presentations

1 Topic Distributions over Links on Web Jie Tang 1, Jing Zhang 1, Jeffrey Xu Yu 2, Zi Yang 1, Keke Cai 3, Rui Ma 3, Li Zhang 3, and Zhong Su 3 1 Tsinghua.

Advertisements

A GUIDE TO CREATING QUALITY ONLINE LEARNING DOING DISTANCE EDUCATION WELL.

Incorporating Participant Reputation in Community-driven Question Answering Systems Liangjie Hong, Zaihan Yang and Brian D. Davison Computer Science and.

Personalized Query Classification Bin Cao, Qiang Yang, Derek Hao Hu, et al. Computer Science and Engineering Hong Kong UST.

Automatic Question Generation from Queries Natural Language Computing, Microsoft Research Asia Chin-Yew LIN

Question Identification on Twitter 1 The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2 Google Research, Beijing, China 3 AT&T Labs Research,

An Approach to Evaluate Data Trustworthiness Based on Data Provenance Department of Computer Science Purdue University.

1 Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags Chin-Yu Huang, Chu-Ti Lin, Sy-Yen Kuo, Michael R.

Chen Cheng1, Haiqin Yang1, Irwin King1,2 and Michael R. Lyu1

Learning Maximum Likelihood Bounded Semi-Naïve Bayesian Network Classifier Kaizhu Huang, Irwin King, Michael R. Lyu Multimedia Information Processing Laboratory.

Efficient Convex Relaxation for Transductive Support Vector Machine Zenglin Xu 1, Rong Jin 2, Jianke Zhu 1, Irwin King 1, and Michael R. Lyu 1 4. Experimental.

Cooperative Caching Middleware for Cluster-Based Servers Francisco Matias Cuenca-Acuna Thu D. Nguyen Panic Lab Department of Computer Science Rutgers University.

I.1 ii.2 iii.3 iv.4 1+1=. i.1 ii.2 iii.3 iv.4 1+1=

J. Chen, O. R. Zaiane and R. Goebel An Unsupervised Approach to Cluster Web Search Results based on Word Sense Communities.

I.1 ii.2 iii.3 iv.4 1+1=. i.1 ii.2 iii.3 iv.4 1+1=

Huizhong Doan, Yonbo Cao, Chin-Yew Lin and Yong Yu Shanghai Jiao Tong University & MSRA ACL /7/91Rick Liu.

Quality-aware Collaborative Question Answering: Methods and Evaluation Maggy Anastasia Suryanto, Ee-Peng Lim Singapore Management University Aixin Sun.

An Automatic Segmentation Method Combined with Length Descending and String Frequency Statistics for Chinese Shaohua Jiang, Yanzhong Dang Institute of.

Fenglong Ma1, Yaliang Li1, Qi Li1, Minghui Qiu2,

SIGIR’09 Boston 1 Entropy-biased Models for Query Representation on the Click Graph Hongbo Deng, Irwin King and Michael R. Lyu Department of Computer Science.

How good are your testers? An assessment of testing ability Liang Huang, Chris Thomson and Mike Holcombe Department of Computer Science, University of.

1 Part 5: Closing Task 1 Kelvin asked you to help him prepare and present the final project presentation. Prepare a short presentation to summarize result.

Topic Models in Text Processing IR Group Meeting Presented by Qiaozhu Mei.

We introduce the use of Confidence c as a weighted vote for the voting machine to avoid low confidence Result r of individual expert from affecting the.

1 Formal Models for Expert Finding on DBLP Bibliography Data Presented by: Hongbo Deng Co-worked with: Irwin King and Michael R. Lyu Department of Computer.

Question Identification on Twitter Baichuan Li, Xiance Si, Michael R. Lyu, Irwin King, and Edward Y. Chang 10/9/20151.

Topical Crawlers for Building Digital Library Collections Presenter: Qiaozhu Mei.

11 Learning to Suggest Questions in Online Learning to Suggest Questions in Online Forums Tom Chao Zhou, Chin-Yew Lin, Irwin King Michael R.

Crowdsourcing for Spoken Dialogue System Evaluation Ling 575 Spoken Dialog April 30, 2015.

Online Learning for Collaborative Filtering

Self Organization of a Massive Document Collection Advisor : Dr. Hsu Graduate : Sheng-Hsuan Wang Author : Teuvo Kohonen et al.

ICML2004, Banff, Alberta, Canada Learning Larger Margin Machine Locally and Globally Kaizhu Huang Haiqin Yang, Irwin King, Michael.

Introduction to Web Applications Programming Lab : II MS (IT)

Finding the Hidden Scenes Behind Android Applications Joey Allen Mentor: Xiangyu Niu CURENT REU Program: Final Presentation 7/16/2014.

Location-Based Topic Evolution Haiqin Yang, Shouyuan Chen, Michael R. Lyu, Irwin King The Chinese University of Hong Kong 1.

Improving Web Search Results Using Affinity Graph Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, Wei-Ying Ma Microsoft Research.

Jun Li, Peng Zhang, Yanan Cao, Ping Liu, Li Guo Chinese Academy of Sciences State Grid Energy Institute, China Efficient Behavior Targeting Using SVM Ensemble.

Exploit of Online Social Networks with Community-Based Graph Semi-Supervised Learning Mingzhen Mo and Irwin King Department of Computer Science and Engineering.

Probabilistic Models for Discovering E-Communities Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, Hongyuan Zha The Pennsylvania State University WWW.

A Classification-based Approach to Question Answering in Discussion Boards Liangjie Hong, Brian D. Davison Lehigh University (SIGIR ’ 09) Speaker: Cho,

Hongbo Deng, Michael R. Lyu and Irwin King

Recommender Systems with Social Regularization Hao Ma, Dengyong Zhou, Chao Liu Microsoft Research Michael R. Lyu The Chinese University of Hong Kong Irwin.

11 A Classification-based Approach to Question Routing in Community Question Answering Tom Chao Zhou 1, Michael R. Lyu 1, Irwin King 1,2 1 The Chinese.

Analyzing and Predicting Question Quality in Community Question Answering Services Baichuan Li, Tan Jin, Michael R. Lyu, Irwin King, and Barley Mak CQA2012,

Poster Spotlights Conference on Uncertainty in Artificial Intelligence Catalina Island, United States August 15-17, 2012 Session: Wed. 15 August 2012,

MMM2005The Chinese University of Hong Kong MMM2005 The Chinese University of Hong Kong 1 Video Summarization Using Mutual Reinforcement Principle and Shot.

Enhanced hypertext categorization using hyperlinks Soumen Chakrabarti (IBM Almaden) Byron Dom (IBM Almaden) Piotr Indyk (Stanford)

CS791 - Technologies of Google Spring A Webbased Kernel Function for Measuring the Similarity of Short Text Snippets By Mehran Sahami, Timothy.

UNIT 14 1 Websites. Starter 2 1 Starter 1 Divide the students into groups. Ask them to make lists. Ask Students to read their lists. Discuss the most.

Reputation-aware QoS Value Prediction of Web Services Weiwei Qiu, Zhejiang University Zibin Zheng, The Chinese University of HongKong Xinyu Wang, Zhejiang.

Wen Chan 1 ， Jintao Du 1, Weidong Yang 1, Jinhui Tang 2, Xiangdong Zhou 1 1 School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan.

The Chinese University of Hong Kong Learning Larger Margin Machine Locally and Globally Dept. of Computer Science and Engineering The Chinese University.

Hao Ma, Dengyong Zhou, Chao Liu Microsoft Research Michael R. Lyu

Topic Modeling for Short Texts with Auxiliary Word Embeddings

WSRec: A Collaborative Filtering Based Web Service Recommender System

Next Question Prediction

Video Summarization by Spatial-Temporal Graph Optimization

Hardware / Software Intro

Zhenjiang Lin, Michael R. Lyu and Irwin King

RECOMMENDER SYSTEMS WITH SOCIAL REGULARIZATION

WorkShop on Community Question Answering on the Web

Mr. K’s blueprint for experiment success.

Topic Models in Text Processing

Solving Equations 3x+7 –7 13 –7 =.

Category-Sensitive Question Routing in Community Question Answering

A Classification-based Approach to Question Routing in Community Question Answering Tom Chao Zhou 22, Feb, 2010 Department of Computer.

Mingzhen Mo and Irwin King

Three steps are separately conducted

Web Page Classification with Heterogeneous Data Fusion

Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu

Presentation transcript:

Question Routing in Community Question Answering: Putting Category in Its Place 1 The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2 AT&T Labs Research, San Francisco, CA,USA {bcli, king, Baichuan Li 1, Irwin King 12, and Michael R. Lyu 1 Question Routing Many questions in CQA services are not solved timely Routing new posted questions to users who are most likely to give answers in a short period of time Appropriate users: expertise estimation Language Models (LMs) Problem Irrelevant information For an answerer, a complete set of questions the answerer has answered is utilized in the models Question category Screening irrelevant questions of an answerer Enhancing the efficiency of expertise estimation ii. Transferred Category-Sensitive LM II. Models (cont.)I. Motivations II. Models ~ An investigation of applying question category to question routing in CQA services Two simple but efficient category-sensitive LMs were proposed for estimating answerer expertise Results of experiments have proven that higher accuracies with lower costs are achieved due to the inclusion of question category IV. Experiments V. Conclusions Data Crawled from Computers & Internet and Entertainment & Music categories of Yahoo! Answers Platform PC with 2.4 GHz dual-core CPU, 3G memory Home Computers & Internet Entertainment & Music Software Internet Facebook Google Programming & Design Music Movies Blues ClassicalCountry i. Basic Category-Sensitive LM the question texts of all previously answered questions in c j for u i the leaf category of the new question q r Disadvantages of BCS-LM Based on the same-leaf-category assumption, with potential answerers under similar leaf categories being omitted Answerers with expertise in “Programming & Design” may also be an expert on questions asked in “Software” Estimating category-category transferring probability Answerer-based approach If there are many same answerers posting answers in two categories, these two categories should be similar with each other Category-answerer matrix E Each row represents one leaf category Each column represents one answer e ji : the number of answers u i provided in category c j iii. Compared Models Cluster-based LM (Zhou et al. 2009) Similar questions under same topic are clustered Each leaf category could be treated as a cluster Answerer expertise is estimated through calculating answerer’s contribution to each cluster and the similarity between the routed question and each cluster Mixture of LDA and QLLM (Liu et al. 2010) latent topics VS explicit categories MethodMRRMAPMQRT QLLM BCS-LM (↑29.66%) (↑33.08%) TCS-LM (↑34.59%) (↑37.29%) CBLM LDALM (↑16.10%) (↑19.72%) Figure 2. Different methods’ in QR versus various Ks Figure 1. Part of category hierarchy in Yahoo! Answers Higher Accuracies + Lower Costs Table 2. MRR, MAP and MQRT of various methods Table 1. Description of data set