Jian Jiang

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Jian Jiang is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Automatically building templates for entity summary construction LDA
Pattern mining
Summary template
Information Processing and Management English 2013 In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Finally, we use the generated templates to construct summaries for new entities. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We apply our method on five Wikipedia entity categories and compare our method with three baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method. © 2012 Elsevier Ltd. All rights reserved. 0 0
Query-oriented keyphrase extraction Informativeness
Keyphrase extraction
Language model
Phraseness
Lecture Notes in Computer Science English 2012 People often issue informational queries to search engines to find out more about some entities or events.While aWikipedia-like summary would be an ideal answer to such queries, not all queries have a corresponding Wikipedia entry. In this work we propose to study query-oriented keyphrase extraction, which can be used to assist search results summarization. We propose a general method for keyphrase extraction for our task, where we consider both phraseness and informativeness. We discuss three criteria for phraseness and four ways to compute informativeness scores. Using a large Wikipedia corpus and 40 queries, our empirical evaluation shows that using a named entity-based phraseness criterion and a language model-based informativeness score gives the best performance on our task. This method also outperforms two state-of-the-art baseline methods. 0 0
A Research for the Centrality of Article Edit Collective in Wikipedia Wikipedia
Article edit interaction network
Centrality
Networked data mining
Collective intelligence
ICM English 2011 0 0
PITT at TREC 2011 session track Query language model
Relevance feedback
Session
TREC
NIST Special Publication English 2011 In this paper, we introduce our approaches for TREC 2011 session track. Our approaches focus on combining different query language models to model information needs in a search session. In RL1 stage, we build ad hoc retrieval system using sequential dependence model (SDM) on current query. In RL2 stage, we build query language models by combining SDM features (e.g. single term, ordered phrase, and unordered phrase) in both current query and previous queries in the session, which can significantly improve search performance. In RL3 and RL4, we combine query model in RL2 with two different pseudo-relevance feedback query models: in RL3, we use top ranked Wikipedia documents from RL2's results as pseudo-relevant documents; in RL4, snippets of the documents clicked by users in a search session are used. Our evaluation results indicate: texts of previous queries in a session are effective resources for estimating query models and improving search performance; mixing query model in RL2 with the query model estimated using click-through data (in RL4) can improve performance in evaluation setting that considers all subtopics, but no improvement is observed in evaluation setting that considers the only subtopic of current query; our methods of mixing query model in RL2 with query model in RL3 did not improve search performance over RL2 in any of the two evaluation settings. 0 0
Generating templates of entity summaries with an entity-aspect model and pattern mining ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2010 In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We apply our method on five Wikipedia entity categories and compare our method with two baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method. 0 0
Adapting language modeling methods for expert search to rank wikipedia entities Entity ranking
Entity retrieval
Expert search
Language model
Lecture Notes in Computer Science English 2009 In this paper, we propose two methods to adapt language modeling methods for expert search to the INEX entity ranking task. In our experiments, we notice that language modeling methods for expert search, if directly applied to the INEX entity ranking task, cannot effectively distinguish entity types. Thus, our proposed methods aim at resolving this problem. First, we propose a method to take into account the INEX category query field. Second, we use an interpolation of two language models to rank entities, which can solely work on the text query. Our experiments indicate that both methods can effectively adapt language modeling methods for expert search to the INEX entity ranking task. 0 0