TRANS: Transportation

Research Analysis using NLP

TechniqueSHyoungtae Cho, Melissa Egan, Ferhan Ture

Final PresentationDecember 9, 2009

Project Sponsor

Michael PackMichael PackDirector, Center for Advanced Transportation

Technology Laboratory (CATT Lab)University of Maryland

Outlineo Motivationo Goalso Datao Methods

• Clustering• Pairwise similarity

o TRANS Demoo Future worko Conclusions

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.

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.

Outlineo Motivationo Goalso Datao Methods

• Clustering• Pairwise similarity

o TRANS Demoo Future worko Conclusions

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, and2. classify each statement with a topic/category.

– Create visualizations to highlight interesting results.

• E.g., trends in transportation research over the years

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.

Outlineo Motivationo Goalso Datao Methods

• Clustering• Pairwise similarity

o TRANS Demoo Future worko Conclusions

DataPreprocessing

• Extract research needs statements and paper abstracts

Research needs statements

Paper abstracts

ClusteringAn algorithm to group similar data points together

In our work,– Categories of statements and papers not available– IDEA:

1. Use clustering to group similar statements2. Assign a category to each cluster

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

frequency in this documentfrequency in all documents

weight

Tokenization removes stop words, and truncates wordsA weight is computed for each term

only unigrams

k-means ClusteringUser 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 clusterFind the distance from this document to each center

C1

…and assign it to the nearest oneDo the same for each unassigned document

C1C1

C1

C2 C2

C2

The center of cluster 1 is adjusted

C1

C1

C2

C3

C2

C2C2

C2

C3

C3C3

C3

C3

C3C3

C1 C1

C1

C2

C2

Since clusters are not labeled by the algorithm,we look at the most frequent terms and manually decide on namesHighways

ConstructionAdministration

Outlineo Introductiono Motivationo Datao Methods

• Clustering• Pairwise similarity

o TRANS Demoo Future worko Conclusions

0.0no similarity

1.0exactly same

0.28 0.51 0.83

Pairwise SimilarityGiven two documents, compute a similarity score:

– Can be used to detect duplicate work and generate ‘‘more like this’’ lists

– Use same features as clustering

Outlineo Introductiono Motivationo Datao Methods

• Clustering• Pairwise similarity

o TRANS Demoo Future worko Conclusions

Demo– TRANS Java Applet– TRANS Web Application

Future Worko Better Features

• Using N-gram features• Transportation Ontology

o LDA Topic Presentationo Visualization for sub-categorizationoCitation Network Analysis

Conclusiono Implement transportation research Visualization

tool, TRANS• TRANS tool• TRANS Website

o Extend to another academic field