Xiance Si

Xiance Si

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    Preview abstract We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. View details
    Deceptive Answer Prediction with User Preference Graph
    Yang Gao
    Shuchang Zhou
    Decheng Dai
    The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013) (to appear)
    Preview
    Entity Disambiguation with Freebase
    Zhicheng Zheng
    Edward Y. Chang
    Xiaoyan Zhu
    The 2012 IEEE/WIC/ACM International Conference on Web Intelligence (WI'2012) (to appear)
    Preview
    A Data-Driven Approach to Question Subjectivity Identification in Community Question Answering
    Tom Chao Zhou
    Edward Y.
    Irwin King
    Michael R. Lyu
    Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12)(2012)
    Preview
    K2Q: Generating Natural Language Questions from Keywords with User Refinements
    Zhicheng Zheng
    Edward Y. Chang
    Xiaoyan Zhu
    Proceedings of the 5th International Joint Conference on Natural Language Processing, ACL(2011), 947–955
    Preview abstract Garbage in and garbage out. A Q&A system must receive a well formulated question that matches the user’s intent or she has no chance to receive satisfactory answers. In this paper, we propose a keywords to questions (K2Q) system to assist a user to articulate and refine questions. K2Q generates candidate questions and refinement words from a set of input keywords. After specifying some initial keywords, a user receives a list of candidate questions as well as a list of refinement words. The user can then select a satisfactory question, or select a refinement word to generate a new list of candidate questions and refinement words. We propose a User Inquiry Intent (UII) model to de- scribe the joint generation process of keywords and questions for ranking questions, suggesting refinement words, and generating questions that may not have previously appeared. Empirical study shows UII to be useful and effective for the K2Q task. View details
    Question Identification on Twitter, Accepted by CIKM 2011
    Baichuan Li
    Michael R. Lyu
    Irwin King
    Edward Y. Chang
    Proceedings of the 20th ACM international conference on Information and knowledge management, ACM, New York, NY, USA(2011)
    Preview abstract In this paper, we investigate the novel problem of auto- matic question identification in the microblog environment. It contains two steps: detecting tweets that contain ques- tions (we call them “interrogative tweets”) and extracting the tweets which really seek information or ask for help (so called “qweets”) from interrogative tweets. To detect inter- rogative tweets, both traditional rule-based approach and state-of-the-art learning-based method are employed. To extract qweets, context features like short urls and Tweet- specific features like Retweets are elaborately selected for classification. We conduct an empirical study with sampled one hour’s English tweets and report our experimental re- sults for question identification on Twitter. View details
    Confucius and Its Intelligent Disciples: Integrating Social with Search
    Edward Y. Chang
    Zoltan Gyongyi
    Maosong Sun
    Proceedings of VLDB 2010, 36th International Conference on Very Large Data Bases, VLDB Endowment, pp. 1505-1516
    Preview abstract Q&A sites continue to flourish as a large number of users rely on them as useful substitutes for incomplete or missing search results. In this paper, we present our experience with developing Confucius, a Google Q&A service launched in 21 countries and four languages by the end of 2009. Confucius employs six data mining subroutines to harness synergy between web search and social networks. We present these subroutines’ design goals, algorithms, and their effects on service quality. We also describe techniques for and experience with scaling the subroutines to mine massive data sets. View details
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