PAIRS Forming a ranked list using mined, pairwise comparisons Reed A. Coke, David C. Anastasiu, Byron J. Gao

PAIRS Pairwise Automatic Inferential Ranking System dmlab.cs.txstate.edu/pairs

The Problem Given a list of items, as well as an optional attribute, how best to generate a ranked list in an online system What is the fastest way to get an accurate result? What is the most accurate way to get a fast result?

Previous approaches NLP techniques are likely the best Can be very costly time-wise – Especially with nonstandard grammar of internet PAIRS is an attempt at finding a balance between speed and accuracy

Overall Architecture 1. Query Parsing (fast) 2. Comparison Location (slow) 3. Comparison Evaluation (fast) 4. Ranking (fast)

Query Parsing Separates list into pairs: – i.e. (A, B, C)->(A,B), (A,C), (B,C) Leads to rapid explosion of searches Each pair then is expanded into 4 queries – i.e. “A vs. B”, “A, B”, etc. Finally, each query is sent alternatingly through Yahoo and Google, thanks to AbstractSearch2

Comparison Location Text is retrieved from each unique URL in the search results. The text is then sent to a Java program which tags the part of speech of each word. Line by line, the program determines whether or not the sentence is comparative. Experimental results for Comparison Location – PAIRS keyword list: 50% recall, 80% precision – Ganapathibotla & Liu list: 97.7% recall, 32% precision

Location (continued) A comparative sentence is one that meets the following criteria: – Contains a comparative word – Contains both nouns (stemmed) in the pair Special cases: – Pronouns and ellipsis, keep track of “relevancy” of past nouns – Phrases Any comparison is then evaluated immediately

Special Case: Pronouns – Jaguars are big. They are bigger than wolves. – John loves computers. In fact, he loves them more than Sally. – John loves computers. Sally does too. However, he loves them more. – John likes Michael Jordan. He is a much more loyal fan than Sally. – John likes Michael Jordan. He dunks more impressively than Sally. – (on a discussion board) I respectfully disagree with you.

Special Case: Ellipsis – Wolves are big. However, jaguars are bigger. – Wolves are big. Jaguars are bigger. – Wolves are annoying, but don't get me started on coyotes. – Wolves are annoying, but turtles aren’t. – Wolves are annoying and turtles aren’t.

Relevance Dictionary Keep track of all nouns Score is affected by recency and frequency

Comparison Evaluation 86% of time, people mention the noun that they prefer first. – i.e. n1 is better than n2, not n2 is worse than n1 – Better methods have been found, but not quicker ones Ultimately, will need a list of + and – comparisons – This will have to be done by domain: Rocky has fought more than Drago. (+) My son has fought more than your son. (-)

Creating the Ranking Create a graph with weight edges Brute force the score of the path from each node to every other node within the connected component This results in a ranked list for each component

Problems Still slow Query parsing needs experiments to determine just how many queries are needed per pair System is untested as a whole. Must be tested on a closed set of docs to determined total precision/recall Comparison evaluation could be more graceful Graph traversal algorithm could be better

Applications PAIRS has several interesting applications – College decisions – Product comparison – Any sort of popularity contest – Taking a majority vote

Future Research Polishing each component of PAIRS Testing PAIRS on a closed system BridgeFinder

Conclusion PAIRS was built from the ground up. The only pre-programmed component of PAIRS was the Stanford POS tagger. Things I learned about research – How to formulate a research topic – How to research previous work in a topic – Experimentation – How to write a technical report – How to give a presentation

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