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Visually Interpreting Names as Demographic Attributes

In the AAAI 2015 conference, we presented the work “Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data,” a collaboration with a research team in National Taiwan University. This study aims to automatically associate a name and its likely demographic attributes, e.g., gender and ethnicity. More specifically, the associations are driven by web-scale search logs […] → Read More: Visually Interpreting Names as Demographic Attributes

Visually Interpreting Names as Demographic Attributes

In the AAAI 2015 conference, we presented the work “Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data,” a collaboration with a research team in National Taiwan University. This study aims to automatically associate a name and its likely demographic attributes, e.g., gender and ethnicity. More specifically, the associations are driven by web-scale search logs […] → Read More: Visually Interpreting Names as Demographic Attributes

Visually Interpreting Names as Demographic Attributes

In the AAAI 2015 conference, we presented the work “Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data,” a collaboration with a research team in National Taiwan University. This study aims to automatically associate a name and its likely demographic attributes, e.g., gender and ethnicity. More specifically, the associations are driven by web-scale search logs […] → Read More: Visually Interpreting Names as Demographic Attributes

Visually Interpreting Names as Demographic Attributes

In the AAAI 2015 conference, we presented the work “Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data,” a collaboration with a research team in National Taiwan University. This study aims to automatically associate a name and its likely demographic attributes, e.g., gender and ethnicity. More specifically, the associations are driven by web-scale search logs […] → Read More: Visually Interpreting Names as Demographic Attributes

Do Topic-Dependent Models Improve Microblog Sentiment Estimation?

When estimating the sentiment of movie and product reviews, domain adaptation has been shown to improve sentiment estimation performance.  But when estimating the sentiment in microblogs, topic-independent sentiment models are commonly used. We examined whether topic-dependent models improve performance when a large number of training tweets are available. We collected tweets with emoticons for six […] → Read More: Do Topic-Dependent Models Improve Microblog Sentiment Estimation?

Dear Facebook, What is the performance of your face recognizer? Thanks!

As far as I can tell, Facebook must have one of the largest collection of images with face tags. I can’t imagine any Facebook employee with even a few weeks of a machine learning course under their belt hasn’t tried to train a model to perform face recognition on their data.
Does anyone know of [...] → Read More: Dear Facebook, What is the performance of your face recognizer? Thanks!

Finding relevance judgements in the wild

We recently heard our poster on online forum search was accepted to SIGIR 09, and I’ve been wanting to post something about the test setup we used in that study.
There’s no existing IR test collection for such a task, although some similar datasets do exist. For various reasons we weren’t able to create [...] → Read More: Finding relevance judgements in the wild