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User: johnedwards

The Politics of Parsing Hillary Clinton responds with double-talk during the Democratic candidates debate on October 30, 2007. Tags: doubletalk parsing parse Hillary Clinton debate John Edwards Politics of Parsing campaign ad

User: grandtheftcountry

Dana Perino: We're Not Occupying Iraq, We Were Invited! In this clip from C-SPAN, White House spokeswoman Dana Perino tells Helen Thomas that the US isn't occupying Iraq, and that we're there by invitation of the sovereign government of Iraq. Oh, and that we're there under a UN mandate. You know, the one that didn't authorize force? Tags: Dana Perino Iraq Occupation War White House Bush

User: googletechtalks

Forest-based Search Algorithms in Parsing and Machine Translation Google Tech Talks March, 14 2008 ABSTRACT Many problems in Natural Language Processing (NLP) involves an efficient search for the best derivation over (exponentially) many candidates, especially in parsing and machine translation. In these cases, the concept of "packed forest" provides a compact representation of the huge search spaces, where efficient inference algorithms based on Dynamic Programming (DP) are possible. In this talk we address two important open problems within this framework: exact k-best inference which is often used in NLP pipelines such as parse reranking and MT rescoring, and approximate inference when the search space is too big for exact search. We first present a series of fast and exact k-best algorithms on forests, which are orders of magnitudes faster than previously used methods on state-of-the-art parsers such as Collins (1999). We then extend these algorithms for approximate search when the forests are too big for exact inference. We discuss two particular instances of this new method, forest rescoring for MT decoding with integrated language models, and forest reranking for discriminative parsing. In the former, our methods perform orders of magnitudes faster than conventional beam search on both state-of-the-art phrase-based and syntax-based systems, with the same level of search error or translation quality. In the latter, faster search also leads to better learning, where our approximate decoding makes whole-Treebank discriminative training practical and results in the best accuracy to date for parsers trained on the Treebank. This talk includes joint work with David Chiang (USC Information Sciences Institute). Liang Huang (2008). Forest Reranking: Discriminative Parsing with Non- Local Features. Proceedings of ACL 2008 (to appear). http://www.cis.upenn.edu/~lhuang3/forest-rerank.pdf Liang Huang and David Chiang (2007). Forest Rescoring: Faster Decoding with Integrated Language Models. Proceedings of ACL 2007. http://www.cis.upenn.edu/~lhuang3/acl-cube.pdf Liang Huang and David Chiang (2005). Better k-best Parsing. Proceedings of IWPT 2005. http://www.cis.upenn.edu/~lhuang3/huang-iwpt-correct.pdf Speaker: Liang Huang Liang Huang is a final-year PhD student at the University of Pennsylvania, co-supervised by Aravind Joshi and Kevin Knight (USC/ ISI). He is mainly interested in the theoretical aspects of computational linguistics, in particular, efficient algorithms in parsing and machine translation, generic dynamic programming, and formal properties of synchronous grammars. He also works on applying computational linguistics to structural biology. Tags: google techtalks techtalk engedu talk talks googletechtalks education

User: johnedwards

The Politics of Parsing - Part 2 A question from last week's Democratic debate returns -- along with double-speak from Senator Clinton. This clip comes from CNN's Situation Room on November 6, 2007. Tags: doublespeak election candidate Hillary Clinton Democrat John Edwards

User: googletechtalks

Incremental Bayesian Networks for Natural Language Parsing Google Tech Talks August 13, 2007 ABSTRACT Natural language parsing is a particularly challenging structure prediction problem, due to the large space of output structures and the complex nature of the statistical dependencies between features of the output structures. Typically these statistical dependencies are specified by hand, but recently there has been interest in using latent variables to induce them automatically. In this talk I will present a framework for structure prediction with latent variables based on a form of Dynamic Bayesian Network called Incremental Sigmoid Belief Networks (ISBNs), and illustrate how it can be applied to parsing. Approximations to ISBNs have achieved... Tags: google howto incremental bayesian networks

User: googletechtalks

Movie/Script: Alignment and Parsing of Video and Text Transcription Google Tech Talks March, 26 2008 ABSTRACT Timothee Cour - Research Scientist Movies and TV are a rich source of highly diverse and complex video of people, objects, actions and locales "in the wild". Harvesting automatically labeled sequences of actions from video would enable creation of large-scale and highly-varied datasets. To enable such collection, we focus on the task of recovering scene structure in movies and TV series for object/person tracking and action retrieval. We present a weakly supervised algorithm that uses the screenplay and closed captions to parse a movie into a hierarchy of shots and scenes. Scene boundaries in the movie are aligned with screenplay scene labels and shots are reordered into a sequence of long continuous tracks or threads which allow for more accurate tracking of people and actions across shot boundaries. Scene segmentation, alignment, and shot threading are formulated as inference in a unified generative model and a novel hierarchical dynamic programming algorithm that can handle alignment and jump-limited reorderings in linear time is introduced. We present quantitative and qualitative results on movie alignment and parsing, and use the recovered structure for tracking and naming of characters as well as retrieval of common actions in several episodes of popular TV series. If time permits we will also present our recent results on approximate inference with eigenvalue optimization. Speaker: Timothee Cour - Research Scientist Timothee Cour is a fifth year PhD student at the University of Pennsylvania, Philadelphia, in Computer Science. He completed his undergraduate education at the Ecole Polytechnique in France, majoring in Computer Science and Applied Mathematics. His research advisor is Prof. Ben Taskar and he also worked closely with Prof. Jianbo Shi. Tags: google techtalks techtalk engedu talk talks googletechtalks education

User: GoogleDevelopers

Google I/O 2008 - Parsing and Generating KML Parsing and Generating KML with Google's KML Library Michael Ashbridge (Google) KML is a file format used to display geographic data in an earth browser, such as Google Earth, Google Maps and Google Maps for mobile. You can create KML files to pinpoint locations, add image overlays and expose rich data in new ways. This session will introduce Google's open source KML library for working with KML files. We'll explore its architecture and then show you how to parse and generate KML in your applications and scripts. Participants should have basic familiarity with KML. Tags: Google I/O IO2008 KML

User: pycon08

To RE or not to RE - parsing text in Python PyCon 2008 Talk by Anna M Ravenscroft (Stanford University) Text parsing - breaking up text into smaller parts for processing - is a common task for programmers. Whether you're tokenizing a sentence for Part of Speech tagging in computational linguistics, automatically checking logs for specific errors, or doing Hidden Markov Models to output Emily Dickinson-style poems, chances are, at some point in your programming, you'll need to do text parsing. One of the most common methods of doing text parsing uses a specialized pattern-recognition language called regular expressions. Regular expressions (REs) can be intimidating to a new programmer; they may try to avoid REs at all costs. Others will turn to REs out of unfamiliarity with the wonders of Python native string manipulation. This talk will focus on the basics: * when and how can you use Python's native string methods, * when to consider REs, and * how to do simple text parsing. Slides available at http://us.pycon.org/2008/conference/schedule/event/63/ Tags: parsing python newbie regex pycon 2008

User: NewAmericaFoundation

Parsing the Iran Challenge Ruprecht Polenz, a senior CDU Member of the Bundestag, is one of the most powerful German voices on his country's foreign policy and national security policy issues. He has been focused on what is real, what is not, and what policy contours America and Europe should take towards Iran for some time. In addition, his Foreign Affairs Committee determines, with the government and the full Bundestag, whether or not German forces will be deployed, so he is keenly interested in NATO operations in Kosovo and Afghanistan and will speak to these topics in his remarks. Tags: Iran Foreign Policy US NATO Kosovo Afghanistan Europe Germany

User: ucberkeley

CS 61B Lecture 37: Expression Parsing CS 61B: Data Structures - Fall 2006 Instructor Jonathan Shewchuk Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language. http://www.cs.berkeley.edu Tags: CS 61b expression parsing shewchuk ucberkeley lecture

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