Annotating a Foreign
Language Lexical
Resource with Pictures
Dmitry Ustalov
IMM UB RAS / UrFU
Yekaterinburg, Russia

Outline
•Introduction
•Related Work
•Approach
•Evaluation
•Results
•Discussion
•Conclusion
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Introduction
•The problem of mapping images to
the word senses is quite important:
• multimedia search,
• text illustration,
• quality assessment.
•It is also interesting to assess the
Yet Another RussNet lexical resource.
(Braslavski et al, 2014).
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Related Work
• PicNet, a proprietary resource
(Mihalcea & Leong, 2008).
• ImageNet annotates WordNet with
pictures & bounding boxes
(Deng et al., 2009).
• Intersection with WordNet.ru is negligible.
• ImageCLEF creates software and datasets
for image indexing (Mü̈ller et al., 2010).
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Related Work: Flickr
•Single-query image retrieval
(Reiter et al., 2007).
•Semantic Web-based approach
(Trojahn et al., 2008).
•Wikipedia-based approach
(Stampouli et al., 2010).
•Flickr tags with visual saliency of
images (Jiang et al., 2014).
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Problem
Given an annotated image I, a bilingual
dictionary B, and a lexical resource S,
produce a mapping Is.
“cat”,“tomcat”,“kitten” →
«кот, кошка, котёночек»
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TagBag: Assumptions
•The most image tags are nouns.
•Tags may be polysemous and the
redundant tags may be present.
• “crane” is «журавль» or «кран»?
•The image has a “main” object.
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TagBag
•Tag. Initialize an empty vector.
• Iterate over image tags and retrieve all
the translations for each tag.
• Add each occurrence to a dimension.
•Bag. Prune that vector.
• Remove the low frequency dimensions
with the cut-off value.
• Return the resulting vector.
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TagBag: Pseudocode
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Evaluation
•The present approach is pretty simple.
Let’s evaluate it empirically.
•Take the top 1500 English nouns and
search for Flickr photos.
http://www.talkenglish.com/Vocabulary/T
op-1500-Nouns.aspx
•Get the V.K. Mueller’s dictionary.
http://ustalov.imm.uran.ru/pub/mueller.tar.gz
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Experimental Setup
•Yet Another RussNet (CC BY-SA).
http://russianword.net/
•Similarity measures:
• cosine similarity,
• Jaccard index.
•Ask three
annotators to
submit
judgements.
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призрак, тень, намёк
12https://www.flickr.com/photos/127324269@N03/16217604730

труд, работа, занятие
13https://www.flickr.com/photos/79304587@N07/16192772090

мужчина, парень, юноша
14https://www.flickr.com/photos/94029069@N03/15797009873

футбол
15https://www.flickr.com/photos/113780395@N05/15789001293

пища, провизия, питание, корм
16https://www.flickr.com/photos/80972943@N00/16396295195

Results
•The accuracy is moderately high and
the agreement level is good.
•Both measures demonstrate the same
performance.
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http://ustalov.imm.uran.ru/pub/tagbag-aist.tar.gz

Discussion
•Some mappings are the same w.r.t.
the similarity measures and 13 of 43
of these mapping are wrong.
•Three sources of errors:
• sloppy image tags (7 of 13),
• actual mapping errors (3 of 13),
• batch uploads (3 of 13).
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Conclusion
•TagBag is an unsupervised approach
for mapping images to synsets.
•The performance depends both on
image tags and ontology bias.
•Visual saliency and spam filtering may
increase the quality.
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Further Work
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Thank you!
Dmitry Ustalov
a post-graduate student @
IMM UB RAS, Yekaterinburg, Russia.
https://ustalov.name/
dau@imm.uran.ru
The present work is supported by the Russian Foundation
for the Humanities, project no. 13-04-12020.
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