25.3622, Diss: English, Spanish; Comp Ling, Text/Corpus Ling, Translation: Burgos H: 'Towards an image-term co-occurence model...'

[The LINGUIST List]

LINGUIST List: Vol-25-3622. Mon Sep 15 2014. ISSN: 1069 - 4875.

Subject: 25.3622, Diss: English, Spanish; Comp Ling, Text/Corpus Ling, Translation: Burgos H: 'Towards an image-term co-occurence model...'

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Date: Mon, 15 Sep 2014 21:46:15
From: Diego Burgos H. [burgosda at wfu.edu]
Subject: Towards an image-term co-occurence model for multilingual terminology alignment and cross-language image indexing

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Institution: Universitat Pompeu Fabra 
Program: Linguistic Sciences and Applied Linguistics 
Dissertation Status: Completed 
Degree Date: 2014 

Author: Diego A. Burgos H.

Dissertation Title: Towards an image-term co-occurence model for multilingual
terminology alignment and cross-language image indexing 

Dissertation URL:  http://www.tdx.cat/handle/10803/145644

Linguistic Field(s): Computational Linguistics
                     Text/Corpus Linguistics
                     Translation

Subject Language(s): English (eng)
                     Spanish (spa)


Dissertation Director(s):
Leo Wanner

Dissertation Abstract:

This thesis addresses the potential that the relation between terms and images
in multilingual specialized documentation has for glossary compilation,
terminology alignment, and image indexing. It takes advantage of the recurrent
use of these two modes of communication (i.e., text and images) in digital
documents to build a bimodal co-occurrence model which aims at dynamically
compiling glossaries of a wider coverage. The model relies on the developments
of content-based image retrieval (CBIR) and text processing techniques. CBIR
is used to make two images from different origin match, and text processing
supports term recognition, artifact noun classification, and image-term
association. The model aligns one image with its denominating term from
collateral text, and then aligns this image with another image of the same
artifact from a different document, which also enables the alignment of the
two equivalent denominating terms. The ultimate goal of the model is to tackle
the limitations and drawbacks of current static terminological repositories by
generating bimodal, bilingual glossaries that reflect real usage, even when
terms and images may originate from noisy corpora.







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