1. A research literature search engine with abbreviation recognition
Cheng-Tao Chu
Pei-Chin Wang

2. Outline Features Demo Issues involved Implementation Evaluation Q&A
Tailored Edit Distance
Probabilistic Model
Translation Model
Score Combination
Evaluation
Q&A

3. Features
Given a query containing authors, proceeding or title keywords, return relevant papers
Able to retrieve the desired papers with abbreviated author/proceeding names
Web interface for query and user evaluation.

4. Demo
It’s show time

5. Issues involved
Tag the arbitrary query into author, proceeding, and other keywords fields
Recognize author
P. Raghavan -> Prabhakar Raghavan
-> Padma Raghavan
-> … Raghavan
Probability of each possible candidates

6. Issues involved (cont.)
Recognize proceeding name
More than a look-up table
IJCAI -> International Joint Conference of AI
-> IJCAI Workshop
How to combine the weight of each candidate
Score from Lucene
Score for a possible author
Score for a possible proceeding

7. Implementation DBLP XML Parser Tagger Database Query Browser Search
Engine
Retrieved
Documents
Probabilistic
Model
Tailored
Edit Distance

8. Tailored Edit Distance
Heuristic
Award for consecutive matching
Award for matching capitalized character
More penalty on substitution, less on insertion/deletion
Probabilistic representation
Transform edit distance cost to probability
Normalize the cost
Use training data to estimate the distribution

9. Conceptual Histogram

10. Probabilistic Model Translation Model Network Structure
Use tailored edit distance to estimate the distribution
Return a distribution of candidate names (Assuming the independency between the full name and its abbreviation given evidence)
Network Structure
Full Name
First Name
Middle Name
Last Name
First Ini.
Mid. Ini.
Last Ini.

11. Score Combination Lucene score formula
Assign weights to each candidates as
Combination score
Set idf(t) as ( weight of that term + original idf(t) )
Assign boost value to each term in query

12. Evaluation Test data construction Evaluation by test data
precision
User evaluation
Comparison with Google Scholar

13. Q&A