Dr. Ellen Voorhees, Director of TREC, over at NIST.gov informed me by email of this Call for Papers. Over the years, I have received invitations to several TREC tracks and no doubt that the groups that conform these are a great place to be.
For those that want to submit manuscript, here is the full Call:
Here is a snake preview of the June issue of IR Watch. If you are a subscriber it will arrive to your inbox over the weekend or at the latest by Monday.
Enjoy it!
What is Data Mining? Good question.
After a great one week vacation away from the blog, it is good to be back. During my vacation I was asked to explain the difference between data mining and information retrieval; so this post goes.
Here is a standard definition I wrote for a graduate course syllabus to be taught next fall at a local university:
The simplest litmus test I have come with to know if a translation software is free from flaws consists in checking its output under a recursive translation between two languages. I like to call this “iterlation”. I like Spanish and English, so these are my preferred languages.
To iterlate text I normally do this. Defining x as an iteration step, do this:
Dr. Mark Green, Director of the Institute of Pure and Applied Mathematics at UCLA (IPAM) informed me by email of the upcoming workshops IPAM is organizing. I meet Dr. Green last year during a one-week workshop they organized (The Document Space Workshop)
I am listing below the new workshops relevant to search engines:
If you are an IR researcher looking for some open source software this post is for you.
During the second day of the OJOBuscador Congress 2.0 held in Madrid, Spain (March 8, 9) I attended the IR with Usability track.
The first speaker was Dr. Carlos Castillo from Yahoo! Research Spain. He presented on IR with Adversarial and Web Spam.
Then the next speaker and IR practitioner, Jose Ramon-Perez Aguera, presented on several open source software. I want to share with you a handly list, thanks to Jose’s presentation:
How do users and machines assess relevance? That is, how do they determine what is/is not relevant content upon a given information need like a query?
For users, this is mostly a visual experience. By contrast for machine this is mostly a parsing experience. So we end up with a fascinating machine learning problem.
