User:SBaker43

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en
This user is a native English speaker.
Category:User en#SBaker43Category:User en-N#SBaker43
de-1
Dieser Benutzer hat grundlegende Deutschkenntnisse.
Category:User de#SBaker43Category:User de-1#SBaker43
es-0
Este usuario no entiende el español (o lo entiende con mucha dificultad).
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My native home page is on my en user page.

I'm often adding sub-categories and distributing images to sub-categories for topics I'm familiar with or interested in. Helping categorize the Lewis Hine TVA photographs was both interesting and educational.

"There is no limit to what a man can do or where he can go if he doesn't mind who gets the credit."

If you need to communicate about a Commons issue, you may leave me a message here on

Commons or on the
English Wikipedia.
10,000enThis user has over 10,000 edits on English Wikipedia.

Commons work

This user has been on Wikimedia Commons for
15 years, 8 months and 18 days
13,000+This user has made over 13,000 contributions to Commons. (total)
This user comes from United States.
Category:Users in the United States#SBaker43
UTC+6This user's timezone is UTC+6.
Category:User UTC+6#SBaker43
This user is an IT professional.
Pending tasks for SBaker43: edit this list - add to watchlist - purge


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  • {{self|author=[[:en:User:SBaker43|SBaker43]] at [http://en.wikipedia.org en.wikipedia]|Cc-by-sa-3.0|GFDL}}

Commons:Quality images/Recently promoted

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Support template + other links
Flickr Place Extraction - ACM DL

Methods for extracting place semantics from Flickr tags

We describe an approach for extracting semantics for tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place semantics for tags that are assigned to photos on Flickr, a popular-photo sharing Web site that supports location (latitude/longitude) metadata for photos. We propose the adaptation of two baseline methods, inspired by well-known burst-analysis techniques, for the task; we also describe two novel methods, TagMaps and scale-structure identification. We evaluate the methods on a subset of Flickr data. We show that our scale-structure identification method outperforms existing techniques and that a hybrid approach generates further improvements (achieving 85% precision at 81% recall). The approach and methods described in this work can be used in other domains such as geo-annotated Web pages, where text terms can be extracted and associated with usage patterns.
Category:User UTC+6 Category:User de Category:User de-1 Category:User en Category:User en-N Category:Users in the United States