Daniel Marcu

Daniel Marcu is Director of Strategic Initiatives at the Information Sciences Institute and Research Associate Professor in the Department of Computer Science at the University of Southern California.

  • The Theory and Practice of Discourse Parsing and Summarization

    The Theory and Practice of Discourse Parsing and Summarization

    Daniel Marcu

    This book documents the first serious attempt to construct automatically and use nonsemantic computational structures for text summarization.

    Until now, most discourse researchers have assumed that full semantic understanding is necessary to derive the discourse structure of texts. This book documents the first serious attempt to construct automatically and use nonsemantic computational structures for text summarization. Daniel Marcu develops a semantics-free theoretical framework that is both general enough to be applicable to naturally occurring texts and concise enough to facilitate an algorithmic approach to discourse analysis. He presents and evaluates two discourse parsing methods: one uses manually written rules that reflect common patterns of usage of cue phrases such as "however" and "in addition to"; the other uses rules that are learned automatically from a corpus of discourse structures. By means of a psycholinguistic experiment, Marcu demonstrates how a discourse-based summarizer identifies the most important parts of texts at levels of performance that are close to those of humans.

    Marcu also discusses how the automatic derivation of discourse structures may be used to improve the performance of current natural language generation, machine translation, summarization, question answering, and information retrieval systems.

    • Hardcover $45.00

Contributor

  • Predicting Structured Data

    Predicting Structured Data

    Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, and S.V.N Vishwanathan

    State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

    Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.

    Contributors Yasemin Altun, Gökhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumé III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pérez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schölkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston

    • Hardcover $47.00
    • Paperback $45.00