Interesting Reads
July 26, 2012
There is a very enlightening source of reading references to be found in Jeff Huang’s “Best Paper Awards in Computer Science.” He conveniently provided a list of informative papers neatly categorized by area of expertise, like artificial intelligence or human computer interaction.
While scrolling down the list, two interesting papers seemed to jump right out.
The first of which, “Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections,” describes a new approach, as:
“A novel approach for inducing unsupervised part-of-speech taggers for languages that have no labeled training data, but have translated text in a resource-rich language. Our method does not assume any knowledge about the target language (in particular no tagging dictionary is assumed), making it applicable to a wide array of resource-poor languages. We use graph-based label propagation.”
The second paper, “How does search behavior change as search becomes more difficult?” Describes some research on search and their conclusions, with:
“When having difficulty in finding information, users start to formulate more diverse queries, they use advanced operators more, and they spend a longer time on the search result page as compared to the successful tasks. The results complement the existing body of research focusing on successful search strategies.”
Researchers are consistently developing models to predict and understand changes in text entry. Sadly, most of the models fail to account for varying system parameters and the ever changing human factor, nor their evolving relationship.
The latter explains the dumbing of search…but they were interesting reads.
Jennifer Shockley, July 26, 2012
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