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 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

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

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)

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)

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

Software available  MEAD – text summarization  NSIR – question answering  CLAIRLIB – generic NLP/IR