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2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013

作者： Bergsma, Shane Dredze, Mark van Durme, Benjamin Wilson, Theresa Yarowsky, David Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

Hidden properties of social media users, such as their ethnicity, gender, and location, are often reflected in their observed attributes, such as their first and last names. Furthermore, users who communicate with each other often have similar hidden properties. We propose an algorithm that exploits these insights to cluster the observed attributes of hundreds of millions of Twitter users. Attributes such as user names are grouped together if users with those names communicate with other similar users. We separately cluster millions of unique first names, last names, and user-provided locations. The efficacy of these clusters is then evaluated on a diverse set of classification tasks that predict hidden users properties such as ethnicity, geographic location, gender, language, and race, using only profile names and locations when appropriate. Our readily-replicable approach and publicly-released clusters are shown to be remarkably effective and versatile, substantially outperforming state-of-the-art approaches and human accuracy on each of the tasks studied. © 2013 Association for Computational Linguistics

关键词： Location

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PARMA: A predicate argument aligner

PARMA: A predicate argument aligner

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Wolfe, Travis Van Durme, Benjamin Dredze, Mark Andrews, Nicholas Beller, Charley Callison-Burch, Chris De Young, Jay Snyder, Justin Weese, Jonathan Xu, Tan Yao, Xuchen Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States University of Maryland College Park MD United States

ISBN: (纸本)9781937284510

We introduce PARMA, a system for cross-document, semantic predicate and argument alignment. Our system combines a number of linguistic resources familiar to researchers in areas such as recognizing textual entailment and question answering, integrating them into a simple discriminative model. PARMA achieves state of the art results on an existing and a new dataset. We suggest that previous efforts have focussed on data that is biased and too easy, and we provide a more difficult dataset based on translation data with a low baseline which we beat by 17% F1. © 2013 Association for Computational Linguistics.

关键词： Semantics

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What's in a domain? multi-domain learning for multi-Attribute data

What's in a domain? multi-domain learning for multi-Attribut...

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

作者： Joshi, Mahesh Dredze, Mark Cohen, William W. Rosé, Carolyn P. School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781937284473

Multi-Domain learning assumes that a single metadata attribute is used in order to divide the data into so-called domains. However, real-world datasets often have multiple metadata attributes that can divide the data into domains. It is not always apparent which single attribute will lead to the best domains, and more than one attribute might impact classification. We propose extensions to two multi-domain learning techniques for our multi-Attribute setting, enabling them to simultaneously learn from several metadata attributes. Experimentally, they outperform the multi-domain learning baseline, even when it selects the single "best" attribute. © 2013 Association for Computational Linguistics.

关键词： Metadata

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What’s in a Domain? Multi-Domain Learning for Multi-Attribute Data 2

What’s in a Domain? Multi-Domain Learning for Multi-Attribu...

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ISBN: (纸本)9781937284473

Multi-Domain learning assumes that a single metadata attribute is used in order to divide the data into so-called domains. However, real-world datasets often have multiple metadata attributes that can divide the data into domains. It is not always apparent which single attribute will lead to the best domains, and more than one attribute might impact classification. We propose extensions to two multi-domain learning techniques for our multi-attribute setting, enabling them to simultaneously learn from several metadata attributes. Experimentally, they outperform the multi-domain learning baseline, even when it selects the single "best" attribute. © 2013 Association for Computational Linguistics

关键词： Metadata

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Seeded graph matching for correlated Erdös-Rényi graphs

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2014年第1期15卷

作者： Kevin Murphy Bernhard Schölkopf Vince Lyzinski Donniell E. Fishkind Carey E. Priebe Google Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD

Graph matching is an important problem in machine learning and pattern recognition. Herein, we present theoretical and practical results on the consistency of graph matching for estimating a latent alignment function between the vertex sets of two graphs, as well as subsequent algorithmic implications when the latent alignment is partially observed. In the correlated Erdos-Rényi graph setting, we prove that graph matching provides a strongly consistent estimate of the latent alignment in the presence of even modest correlation. We then investigate a tractable, restricted-focus version of graph matching, which is only concerned with adjacency involving vertices in a partial observation of the latent alignment; we prove that a logarithmic number of vertices whose alignment is known is sufficient for this restricted-focus version of graph matching to yield a strongly consistent estimate of the latent alignment of the remaining vertices. We show how Frank-Wolfe methodology for approximate graph matching, when there is a partially observed latent alignment, inherently incorporates this restricted-focus graph matching. Lastly, we illustrate the relationship between seeded graph matching and restricted-focus graph matching by means of an illuminating example from human connectomics.

关键词： Erdos-Rényi graph Frank-Wolfe assignment problem consistency estimation graph matching seeded vertices

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UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems 2

UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems

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2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013

作者： Han, Lushan Kashyap, Abhay Finin, Tim Mayfield, James Weese, Jonathan Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (纸本)9781937284480

We describe three semantic text similarity systems developed for the ∗SEM 2013 STS shared task and the results of the corresponding three runs. All of them shared a word similarity feature that combined LSA word similarity and WordNet knowledge. The first, which achieved the best mean score of the 89 submitted runs, used a simple term alignment algorithm augmented with penalty terms. The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features. c 2013 Association for Computational Linguistics

关键词： Semantics

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KELVIN: a tool for automated knowledge base construction

KELVIN: a tool for automated knowledge base construction

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2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013 - Demonstration Session

作者： McNamee, Paul Mayfield, James Finin, Tim Oates, Tim Lawrie, Dawn Xu, Tan Oard, Douglas W. Johns Hopkins University Human Language Technology Center of Excellence United States University of Maryland Baltimore County United States Loyola University Maryland United States University of Maryland College Park United States

ISBN: (纸本)9781937284473

We present KELVIN, an automated system for processing a large text corpus and distilling a knowledge base about persons, organizations, and locations. We have tested the KELVIN system on several corpora, including: (a) the TAC KBP 2012 Cold Start corpus which consists of public Web pages from the University of Pennsylvania, and (b) a subset of 26k news articles taken from English Gigaword 5th edition. Our NAACL HLT 2013 demonstration permits a user to interact with a set of search-able HTML pages, which are automatically generated from the knowledge base. Each page contains information analogous to the semi-structured details about an entity that are present in Wikipedia Infoboxes, along with hyperlink citations to supporting text. © 2013 Association for Computational Linguistics.

关键词： Websites

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Typicality and Object Reference 35

Typicality and Object Reference

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35th Annual Meeting of the Cognitive Science Society - Cooperative Minds: Social Interaction and Group Dynamics, CogSci 2013

作者： Mitchell, Margaret Reiter, Ehud van Deemter, Kees Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States Computing Science Department University of Aberdeen Scotland AberdeenAB24 3FX United Kingdom

ISBN: (纸本)9780976831891

Does the typicality of an object affect how we identify it? When we produce initial reference to a visible object, we are influenced by a variety of factors, including what is visually salient (bottom-up influences) as well as our previous experiences with the object (top-down influences). In this study, we seek to understand how the top-down influence of typicality affects initial reference to an object. We use real world, everyday objects, and focus on the visual properties of SHAPE and MATERIAL. Our findings suggest that there is a tendency to select the atypical over the typical. But we have only begun to scratch the surface of understanding reference to real world objects. The annotated corpus from this study is made available for future research on modeling reference in visual domains. © CogSci *** rights reserved.

关键词： description reference referring expressions typicality vision

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Comparing and evaluating semantic data automatically extracted from text

Comparing and evaluating semantic data automatically extract...

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2013 AAAI Fall Symposium

作者： Lawrie, Dawn Finin, Tim Mayfield, James McNamee, Paul Computer Science Department Loyola University Maryland Baltimore MD United States University of Maryland Baltimore County Baltimore MD United States Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD United States

ISBN: (纸本)9781577356424

One way to obtain large amounts of semantic data is to extract facts from the vast quantities of text that is now available on-line. The relatively low accuracy of current information extraction techniques introduces a need for evaluating the quality of the knowledge bases (KBs) they generate. We frame the problem as comparing KBs generated by different systems from the same documents and show that exploiting provenance leads to more efficient techniques for aligning them and identifying their differences. We describe two types of tools: entity-match focuses on differences in entitles found and linked;kbdiff focuses on differences in relations among those entities. Together, these tools support assessment of relative KB accuracy by sampling the parts of two KBs that disagree. We explore the usefulness of the tools through the construction of tens of different KBs built from the same 26,000 Washington Post articles and identifying the differences. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Semantics

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What affects patient (dis)satisfaction? Analyzing online doctor ratings with a joint topic-sentiment model

What affects patient (dis)satisfaction? Analyzing online doc...

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2013 AAAI Workshop

作者： Paul, Michael J. Wallace, Byron C. Dredze, Mark Dept. of Computer Science Johns Hopkins University Baltimore MD 21218 United States Center for Evidence-based Medicine Brown University Providence RI 02903 United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21211 United States

ISBN: (纸本)9781577356202

We analyze patient reviews of doctors using a novel probabilistic joint model of topic and sentiment based on factorial LDA (Paul and Dredze 2012). We leverage this model to exploit a small set of previously annotated reviews to automatically analyze the topics and sentiment latent in over 50,000 online reviews of physicians (and we make this dataset publicly available). The proposed model outperforms baseline models for this task with respect to model perplexity and sentiment classification. We report the most representative words with respect to positive and negative sentiment along three clinical aspects, thus complementing existing qualitative work exploring patient reviews of physicians. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Artificial intelligence

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