TU Darmstadt / ULB / TUbiblio

Automatic Identification of Novel Metaphoric Expressions

Do Dinh, Erik-Lân (2013)
Automatic Identification of Novel Metaphoric Expressions.
Technische Universität Darmstadt
Diploma Thesis or Magisterarbeit, Bibliographie

Abstract

Manually annotating novel metaphors in philosophical and historical texts is a difficult and time-consuming task, so any automated method would be welcome. In this work, we implement such a method for novel metaphor identification. The approach uses Gaussian mixture models, the parameters of which are estimated by expectation maximization. We seek to improve the results of this baseline by incorporating selectional preferences, whose violation can be indicative for metaphorical use of a term or phrase. As we intend to find only novel metaphorical expressions, we further refine the results by employing a large diachronic n-gram corpus. We find that incorporating selectional preferences and additional n-gram novelty filtering significantly improves on the baseline results. As an example application, the resulting classifications will then be presented in a web-based tool.

Item Type: Diploma Thesis or Magisterarbeit
Erschienen: 2013
Creators: Do Dinh, Erik-Lân
Type of entry: Bibliographie
Title: Automatic Identification of Novel Metaphoric Expressions
Language: English
Date: June 2013
Place of Publication: Darmstadt
Event Location: Darmstadt, Germany
URL / URN: https://download.hrz.tu-darmstadt.de/media/FB20/Dekanat/Publ...
Abstract:

Manually annotating novel metaphors in philosophical and historical texts is a difficult and time-consuming task, so any automated method would be welcome. In this work, we implement such a method for novel metaphor identification. The approach uses Gaussian mixture models, the parameters of which are estimated by expectation maximization. We seek to improve the results of this baseline by incorporating selectional preferences, whose violation can be indicative for metaphorical use of a term or phrase. As we intend to find only novel metaphorical expressions, we further refine the results by employing a large diachronic n-gram corpus. We find that incorporating selectional preferences and additional n-gram novelty filtering significantly improves on the baseline results. As an example application, the resulting classifications will then be presented in a web-based tool.

Uncontrolled Keywords: UKP_a_LangTech4eHum
Identification Number: TUD-CS-2013-0216
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Last Modified: 06 Nov 2019 14:41
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details