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Xing Wei
Researcher at Yahoo!
Publications - 26
Citations - 2802
Xing Wei is an academic researcher from Yahoo!. The author has contributed to research in topics: Web search query & Web query classification. The author has an hindex of 16, co-authored 26 publications receiving 2661 citations. Previous affiliations of Xing Wei include University of Massachusetts Amherst & IBM.
Papers
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Proceedings ArticleDOI
LDA-based document models for ad-hoc retrieval
Xing Wei,W. Bruce Croft +1 more
TL;DR: This paper proposes an LDA-based document model within the language modeling framework, and evaluates it on several TREC collections, and shows that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.
Proceedings ArticleDOI
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
TL;DR: Topical n-grams as discussed by the authors is a probabilistic model that generates words in their textual order by, for each word, first sampling a topic, then sampling its status as a unigram or bigram, and then sampling the word from a topic-specific unigrams or bigrams distribution.
Proceedings ArticleDOI
Table extraction using conditional random fields
TL;DR: The authors used conditional random fields (CRFs) for table extraction and compared them with hidden Markov models (HMMs) and showed that CRFs support the use of many rich and overlapping layout and language features, and as a result, they perform significantly better than HMMs.
Proceedings ArticleDOI
QuASM: a system for question answering using semi-structured data
TL;DR: A system for question answering using semi-structured metadata, QuASM (pronounced "chasm"), which aims to answer factual questions by exploiting the structure inherent in documents found on the World Wide Web.
Proceedings Article
Dynamic mixture models for multiple time series
Xing Wei,Jimeng Sun,Xuerui Wang +2 more
TL;DR: This paper applies Dynamic Mixture Models (DMMs) for online pattern discovery in multiple time series to two real-world datasets, and achieves significantly better results with intuitive interpretation.