1. TRANS: T ransportation R esearch A nalysis using N LP Technique S Hyoungtae Cho, Melissa Egan, Ferhan Ture Final Presentation December 9, 2009

2. Project Sponsor Michael Pack Director, Center for Advanced Transportation Technology Laboratory (CATT Lab) University of Maryland

3. Outline o Motivation o Goals o Data o Methods Clustering Pairwise similarity o TRANS Demo o Future work o Conclusions

4. Project motivation Project was inspired by issues in the transportation research community. First issue: Researchers in the field, including Michael Pack, have concerns about the inefficient use of funds due to repetitive research in the field. – Many research ideas and projects are repeatedly published with only slight repackaging. – It would be ideal if such projects could be detected at the time of their proposal.

5. Project motivation, continued Second issue: Categorization of research projects within the field. – Useful for: Tracking the amount of research done in each sub-field. Understanding research trends within the community. Bringing researchers with similar interests together. – At the moment, these tasks are partially managed by the Transportation Research Board (TRB), but this is costly and not always effective. – Performing the tasks automatically will produce fast, cheap, and objective results. – Visualizing the results will make interpretation and analysis easier, and will communicate them to a larger portion of the community.

6. Outline o Motivation o Goals o Data o Methods Clustering Pairwise similarity o TRANS Demo o Future work o Conclusions

7. Project goals First goal: Use natural language processing (NLP) techniques to analyze the research statements from past years. – Build a system that can 1.detect statements that are very similar, and 2.classify each statement with a topic/category. – Create visualizations to highlight interesting results. E.g., trends in transportation research over the years

8. Project goals, continued Second goal: Create a web site to collect and analyze research ideas in the field. – Web site should: Allow users to submit research needs statements or ideas. Allow other users to vote on these ideas. Generate appropriate visualizations to summarize research needs and interests.

9. Outline o Motivation o Goals o Data o Methods Clustering Pairwise similarity o TRANS Demo o Future work o Conclusions

10. Data Preprocessing Extract research needs statements and paper abstracts TRB WEBSITE ID, Text, Date 1, … 2, … … 809, … CRAWLER 1 TRB WEBSITE ID, Text, Date, Address 1, … 2, … … 9552, … CRAWLER 2 Research needs statements Paper abstracts

11. Clustering An algorithm to group similar data points together In our work, –Categories of statements and papers not available –IDEA: 1.Use clustering to group similar statements 2.Assign a category to each cluster

12. Features As a global recession of unprecedented scale threatens to engulf much of the United States economy, congress and federal policy- makers have assembled a large package of government stimulus spending that can reverse job losses and revive consumer demand. Economists identify road construction as a good way to create jobs in the short- term and to boost economic productivity in the long-term by lowering transportation costs. As a result, highways feature prominently in the proposed Congressional economic stimulus bill and about $30 billion in new federal money for pavements, bridges, and tunnels is likely to flow to state departments of transportation (DOTs) in 2009 and 2010 global recess unpreced scale threaten engulf unit state economi congress federpolici maker assembllargpackag govern stimulu spend can revers job loss revivconsum demand economist identifi road construct waicreat job short term boost econom product long term lower transport cost result highwaifeaturpromin propos congressioneconomstimulu bill 30 billion new federmonei pavement bridg tunnel like flow state depart transport dot 2009 2010 global = 0.03 recess = 0.7 unpreced = 0.41 … Each document is represented by a vector of feature weights Tokenization removes stop words, and truncates words A weight is computed for each term only unigrams

13. k-means Clustering User chooses number of clusters k (e.g., k=3)k=3 documents randomly selected as ‘centers’ For each document that has not been assigned to a cluster Find the distance from this document to each center C1 …and assign it to the nearest one Do the same for each unassigned document C1 C2 The center of cluster 1 is adjusted C1 C2 C3 C2 C3 C1 C2 Since clusters are not labeled by the algorithm, we look at the most frequent terms and manually decide on names Highways Construction Administration

14. Outline o Introduction o Motivation o Data o Methods Clustering Pairwise similarity o TRANS Demo o Future work o Conclusions

15. 0.0 no similarity 1.0 exactly same 0.280.510.83 Pairwise Similarity Given two documents, compute a similarity score: –Can be used to detect duplicate work and generate ‘‘more like this’’ lists –Use same features as clustering

16. Outline o Introduction o Motivation o Data o Methods Clustering Pairwise similarity o TRANS Demo o Future work o Conclusions

17. Demo –TRANS Java Applet –TRANS Web Application

18. Future Work o Better Features Using N-gram features Transportation Ontology o LDA Topic Presentation o Visualization for sub-categorization o Citation Network Analysis

19. Conclusion o Implement transportation research Visualization tool, TRANS TRANS tool TRANS Website o Extend to another academic field