Kyle Elliot

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Document Type

Co-Authors

Kit W. Cho

University of Houston - Downtown

Tomek Strzalkowski

Rensselaer Polytechnic Institute

Ignacio Cases

Companhia Brasileira de Metalurgia e Mineração (Brazil)

Samira Shaikh

University of North Carolina at Charlotte

Boris Yamrom

Cold Spring Harbor Laboratory

Laurie Beth Feldman

University at Albany, State University of New York

George Aaron Broadwell

University of Florida

Sarah Taylor

California State University, Long Beach

Yuliya Peshkova

Umit Boz

University of Calgary

Cooperative Institutions

University at Albany, State University of New York

Albany State University

New York University

Harbin Institute of Technology

Rensselaer Polytechnic Institute

University of Calgary

Xi'an Jiaotong University

Jilin University of Chemical Technology

Sun Yat-sen University

Third Affiliated Hospital of Sun Yat-sen University

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Robust Extraction of Metaphor from Novel Data

Tomek Strzalkowski George Aaron Broadwell Sarah Taylor Laurie Beth Feldman Samira Shaikh

This article describes our novel approach to the automated detection and analysis of metaphors in text. We employ robust, quantitative language processing to implement a system prototype combined with sound social science methods for validation. We show results in 4 different languages and discuss how our methods are a significant step forward from previously established techniques of metaphor identification. We use Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline.

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Source

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Citations (43)

Computing Affect in Metaphors

Tomek Strzalkowski Samira Shaikh Kit W. Cho George Aaron Broadwell Laurie Beth Feldman

This article describes a novel approach to automated determination of affect associated with metaphorical language. Affect in language is understood to mean the attitude toward a topic that a writer attempts to convey to the reader by using a particular metaphor. This affect, which we will classify as positive, negative or neutral with various degrees of intensity, may arise from the target of the metaphor, from the choice of words used to describe it, or from other elements in its immediate linguistic context. We attempt to capture all these contributing elements in an Affect Calculus and demonstrate experimentally that the resulting method can accurately approximate human judgment. The work reported here is part of a larger effort to develop a highly accurate system for identifying, classifying, and comparing metaphors occurring in large volumes of text across four different languages: English, Spanish, Russian, and Farsi.

Affect

10.3115/v1/w14-2306

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Citations (16)

Discovering Conceptual Metaphors using Source Domain Spaces

Samira Shaikh Tomek Strzalkowski Kit W. Cho Ting Liu George Aaron Broadwell

Samira Shaikh, Tomek Strzalkowski, Kit Cho, Ting Liu, George Aaron Broadwell, Laurie Feldman, Sarah Taylor, Boris Yamrom, Ching-Sheng Lin, Ning Sa, Ignacio Cases, Yuliya Peshkova, Kyle Elliot. Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex). 2014.

George (robot)

10.3115/v1/w14-4725

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Citations (3)

A Technique for Simultaneous Multiple Tag Identification

V.P. Mahadevan Pillai Rafael Martı́nez J. Bleichner Kyle Elliot Sreenivasan Ramasamy Ramamurthy

We present for the first time ever, a technique for simultaneously decoding multiple tag responses (i.e., a collision). Additionally the technique provides a considerable degree of robustness and alleviates the problem of missed tags. A theoretical discussion of the underlying principle is done. We provide simulation results when this technique is used specifically for the EPC Gen 2 protocol; the resultant increase in identification rate is 20% when two tag signals are simultaneously decoded and 30% when three tag signals are simultaneously decoded. Experimental results presented show that the extracted individual tag signals (when there is a collision of two or more tag responses) are virtually identical to the single tag signal. We believe that the signal extraction technique discussed here will eventually be a standard feature of all base stations.

Robustness

SIGNAL (programming language)

Identification

10.1109/autoid.2005.9

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Citations (7)