TAILIEUCHUNG - Báo cáo khoa học: "A Probabilistic Model for Fine-Grained Expert Search"

Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been proposed, including metadata extraction, expert profile building, and formal model generation. However, all of them conduct expert search with a coarse-grained approach. With these, further improvements on expert search are hard to achieve. . | A Probabilistic Model for Fine-Grained Expert Search Shenghua Bao1 Huizhong Duan1 Qi Zhou1 Miao Xiong1 Yunbo Cao1 2 Yong Yu1 Shanghai Jiao Tong University 2Microsoft Research Asia Shanghai China 200240 Beijing China 100080 shhbao summer jackson xiongmiao yyu @ Abstract Expert search in which given a query a ranked list of experts instead of documents is returned has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been proposed including metadata extraction expert profile building and formal model generation. However all of them conduct expert search with a coarse-grained approach. With these further improvements on expert search are hard to achieve. In this paper we propose conducting expert search with a fine-grained approach. Specifically we utilize more specific evidences existing in the documents. An evidence-oriented probabilistic model for expert search and a method for the implementation are proposed. Experimental results show that the proposed model and the implementation are highly effective. 1 Introduction Nowadays team work plays a more important role than ever in problem solving. For instance within an enterprise people handle new problems usually by leveraging the knowledge of experienced colleagues. Similarly within research communities novices step into a new research area often by learning from well-established researchers in the research area. All these scenarios involve asking the questions like who is an expert on X or who knows about X Such questions which cannot be answered easily through traditional document search raise a new requirement of searching people with certain expertise. To meet that requirement a new task called expert search has been proposed and studied intensively. For example TREC 2005 2006 and 2007 provide the task of expert search within the enterprise track. In the TREC setting .

• Báo cáo khoa học
• Probabilistic Model
• Fine Grained Expert Search
• Shenghua Bao
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Journal of Computer Science and Cybernetics
• A fuzzy probabilistic relational database model and algebra
• Database model and algebra
• fuzzy probabilistic triple
• Fuzzy probabilistic relation
• Fuzzy probabilistic functional dependency
• Fuzzy probabilistic relational algebraic operation
• Probability distribution
• Probabilistic triple
• Probabilistic relation
• Probabilistic functional dependency
• Probabilistic relational algebraic operation
• Khoa học máy tính
• Tạp chí khoa học
• Probabilistic Duration Calculus
• Probabilistic Timed Automata
• Model checking
• Markov Decision Process
• Probability interval
• Probabilistic combination strategy
• A Probabilistic Model of Syntactic
• Semantic Acquisition
• Child Directed Utterances and their Meanings
• scientific reports
• model language
• process natural language
• A Probabilistic Answer Type Model
• Christopher Pinchak
• Long Papers
• Mathematics
• Physics
• scientific studies
• natural sciences
• A Probabilistic Model
• Canonicalizing Named Entity Mentions
• ngôn ngữ tự nhiên
• Enhanced word decomposition
• calibrating the decision threshold of probabilistic models
• using a model ensemble
• A probabilistic generative model
• an intermediate constituency dependency representation
• Federico Sangati
• A GENERALIZED PROBABILISTIC
• SEMANTIC MODEL
• AMBIGUITY RESOLUTION
• mô hình ngôn ngữ
• BMC Bioinformatics
• Constraint based model
• Gene network
• Bayesian network
• Model integration
• Probabilistic reasoning
• Belief propagation
• Nuclear engineering and technology
• Large strain nonlinear model of lead rubber bearings
• Beyond design basis earthquakes
• Large strain nonlinear model
• Extreme nonlinear numerical model
• Seismic probabilistic safety assessments
• Lexicon and grammar
• probabilistic tagging
• written English
• Probabilistic Hierarchical Clustering
• Morphological Paradigms
• Burcu Can
• Tree Representations
• Probabilistic Models
• Extended Named Entities Detection
• Improving Probabilistic Latent Semantic Analysis
• Principal Component Analysis
• Ayman Farahat Palo Alto
• A PROBABILISTIC APPROACH TO GRAMMATICAL ANALYSIS
• WRITTEN ENGLISH BY COMPUTER
• Andrew David Beale
• A PROBABILISTIC PARSER
• Roger Garside
• Fanny Leech
• A Probabilistic
• David M
• Magerman
• A Probabilistic Context free Grammar
• Disambiguation in Morphological Parsing
• Genome Biology
• Statistical modeling
• Gene addiction
• Probabilistic modeling framework
• Cancer research
• Probabilistic framework
• Analyzing regional communities
• Social networks
• Geographic characteristics
• ErLink Topic model
• Bayes Classifier
• The naïve Bayes Probabilistic model
• Constructing a Classifier from the probability model
• An application of Naïve Bayes Classifier