Wondering if I am praying for you? 🙂 Not at all! I came across this paper:
May All YourWishes Come True: A Study of Wishes and How to Recognize Them
– Funny name for a computer science paper, eh? I was browsing through the Conference schedule of NAACL-HLT 2009 (North American Chapter of the Association for Computational Linguistics – Human Language Technologies) and found this paper over there. The conference is scheduled to be held from May 31st to June 5th 2009.
So, whats it about?: As the name indicates, this paper aims at developing a wish detector, which will enable extraction of information about “wishes” by people. This can be understood as a branch of sentiment analysis, in the sense that – if you consider a product review, this kind of a tool will let the manufacturers know about the user expectations and desires, which can be kept in mind while developing next versions.
WISH corpus?: Well, it seems, every year, theres something called “balldrop” at Newyork city Times Square, during the new years eve. “In December 2007, the Times Square Alliance, coproducer of the Times Square New Year’s Eve Celebration, launched a Web site called the Virtual Wishing Wall that allowed people around the world to submit their New Year’s wishes. These wishes were then printed on confetti and dropped from the sky at midnight on December 31, 2007 in sync with the ball drop” – says this paper. And, the authors gained access to this Wish corpus, and used this for their work. Ok, to give you an example of a wish: “I want to be the master of the world” is a wish. “Let everybody be happy and prosperous” is a wish… 🙂
Analysis of the WISH corpus: They have analyzed this Corpus according to the Topic and Scope of the wishes, using a pre-formed categorization of Topics (Eg: Love, Happiness, Health, Peace, Money etc..11 categories in all) and Scope (Self, World, family etc… 6 in all). Further, they analyzed these wishes according to geographical location (US and Non-US) and concluded that wishes differ in topic and scope with geographical location (Their results were statistically significant) .
Building Wish Detectors: And, here comes the actual part. Here, for the purpose of a baseline, two simple wish detectors are built first:
1. Manually looking for sentences containing : “I wish…” or “I hope..” etc can give you enough number of wishes from a domain. So, some wish-templates were obtained by analyzing the text patterns in sentences, which can indicate a wish, to make a simple rule based wish classifier. If a sentence matches some rule or pattern to some extent, it is a wish. Else,it is not. The authors say that this method might have a good precision but less recall.
2. “Another simple method for detecting wishes is to train a standard word-based text classifier using the labeled training set in the target domain.” – This might have a better recall, but lower precision.
Now, enter the dragon: The authors a method of automatically discovering the wish templates and a learning mechanism to learn the wish template features. They have tested this classifier on various domains. This training was done using WISH corpus and the testing was done on product reviews and politics!
Pretty interesting paper, though i did not get a “Wow!” feeling. The concept of domain independent wish template generation interested me, though.
For enthusiasts, the paper can be read here.