1. Text summarization MEAD NewsInEssence Cross-document structure Sentence compression Lexrank Political science Discourse dynamics Centrality identification Information retrieval Blog databases Question answering Fact extraction Machine learning Graph-based learning Semi-supervised learning Harmonic functions Monte Carlo methods Information extraction Language modeling Modeling burstiness Biomedical literature analysis Citation network analysis Recognizing protein interactions in text Clustering CLAIR: Computational Linguistics And Information Retrieval Machine translation Syntax-based alignment Text generation Syntax-based features Models of the Web Lexical network models Miscellaneous Language reuse Paraphrase identification Lexical models of the Web Dependency parsing Write to radev@umich.edu if you have any questions Courses Information Retrieval (SI 650) – Fall 05 Advanced NLP/IR (EECS 767/SI 767) – Winter 06 Natural Language Processing (EECS 595/SI 661) – Fall 06 Language and Information (EECS 597/SI 760) – Fall 06 Database Applications Design (SI 654) – Fall 05 Faculty: Dragomir Radev Students: Güneş Erkan, Arzucan Özgür, Xiaodong Shi, Zhuoran Chen Mark Joseph, Konstantin Zak, Tony Fader, Joshua Gerrish

2. Main areas of interest  Graph-based methods  Machine learning  Text summarization  Question answering  Text mining in political science, blogometrics, bioinformatics

3. List of current funded projects BlogoCenter: Infrastructure for Collecting, Mining and Accessing Blogs NSF (joint with Junghoo Cho of UCLA) Probabilistic and link-based Methods for Exploiting Very Large Textual Repositories NSF Representing and Acquiring Knowledge of Genome Regulation NIH (joint with Steve Abney, David States, and H.V. Jagadish) Collaborative research: semantic entity and relation extraction from Web-scale text document collections NSF (joint with Michael Collins of MIT and Steve Abney) DHB: The dynamics of Political Representation and Political Rhetoric NSF (joint with Kevin Quinn of Harvard, Burt Monroe of PSU) NCIBI: National center for integrative bioinformatics NIH (joint with 20 other faculty)

4. Representative recent papers  News to Go: Hierarchical Text Summarization for Mobile Devices (SIGIR 2006)  Language Model Based Document Clustering Using Random Walks (HLT- NAACL 2006)  An automated method of topic-coding legislative speech over time with application to the 105th-108th u. s. senate (MPSA 2006 – Gosnell Award)  Summarizing online news topics (CACM 2005)  Using random walks for question-focused sentence retrieval (HLT-EMNLP 2005)  Context-based generic cross-lingual retrieval of documents and automated summaries (JASIST 2005)  Probabilistic question answering on the web (JASIST 2005)  Centroid-based summarization of multiple documents (IPM 2004)  A smorgasbord of features for statistical machine translation (HLT-NAACL 2004)  Graph-based centrality as salience in text summarization (JAIR 2004)

5. Papers in progress or under submission  Summarization evaluation in a cross-lingual information retrieval context. Submitted to Information Processing and Management.  Retrieval of context-specific, dynamic information: A survey of related work. Submitted to ACM Computing Surveys.  Single-document and multi-document summary evaluation using relative utility. Submitted to Information Retrieval.  Exploring Fact-Focused Relevance and Novelty Detection, submitted to Information Processing and Management  Hierarchical Summarization for Delivering Information to Mobile Devices, submitted to Decision Support Systems  Modeling Burstiness in Discourse Using a Stochastic Stack  A topological analysis of semisupervised graph-based learning with harmonic functions  Protein-protein interaction with no external knowledge  An empirical analysis of 100 lexical networks  Hiring networks in information science and computer science  Blind men and elephants: What do citation summaries tell us about a research article  Reinforcement classifiers  Dependency parsing using random walks  Modeling Document Dynamics: An Evolutionary Approach  Cross-document relationship classification for text summarization

6. Software available  MEAD – text summarization  NSIR – question answering  CLAIRLIB – generic NLP/IR radev@umich.edu