1 / 12

CS224N Final Project Geo-location Route Recognition

CS224N Final Project Geo-location Route Recognition. Yingjie (Roger) Zheng Philip (Tony) Hairr June 9, 2010. Objective. We would like that our system can extract a list of locations from web pages that represents the direction of the route and plot the route on a map. Example.

tanner
Download Presentation

CS224N Final Project Geo-location Route Recognition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS224N Final ProjectGeo-location Route Recognition Yingjie (Roger) Zheng Philip (Tony) Hairr June 9, 2010

  2. Objective • We would like that our system can extract a list of locations from web pages that represents the direction of the route and plot the route on a map.

  3. Example From www.lonelyplanet.com

  4. Pipeline Acquire webpage Crawler Recognize place names & organization names NER Get word dependencies Parser Arrange route Route Disambiguate Engine Get coordinates & draw map Map Renderer

  5. From Typed Dependency to RoutePrepositional Phrase • I took a bus ride to Sacramento from Chicago. nsubj(took-2, I-1) det(ride-5, a-3) nn(ride-5, bus-4) dobj(took-2, ride-5) prep(took-2, to-6) pobj(to-6, Sacramento-7) prep(took-2, from-8) pobj(from-8, Chicago-9) From To Chicago Sacramento

  6. From Typed Dependency to RouteTransitive Verbs • I left Palo Alto for New York this morning. nsubj(left-2, I-1) dobj(left-2, Palo_Alto-3) prep(Palo_Alto-3, for-4) pobj(for-4, New_York-5) det(morning-7, this-6) tmod(left-2, morning-7) From To Palo Alto New York

  7. Evaluation • Score = • Precision: We generate lists of unique places appearing in the test program output and the golden test data separately, then match them to find out how many locations appear in both, then calculated precision using the matching and total line counts. • Recall: We calculate recall by dividing the matching lines by the total lines in the golden test data. locations in the golden test data locations in the golden test data + edit distance

  8. Test and Results • Data • Forum data from www.lonelyplanet.com • Baseline • Start and end point according to the order of appearance • Method • Look five sentences in a forum page • Result

  9. Example Output

  10. Example Output Locations Golden Route San Cristobal de las Casas Tuxla Gutierrez Mexico City San Miguel de Allende Output Route San Cristobal de las Casas San Miguel de Allende San Cristobal de las Casas San Miguel de Allende Oaxaca San Cristobal San Cristobal Mexico City San Miguel

  11. Problems and Future Work Crawler Precision and Recall of the NER system NER How to recognize different routes in one document according to context Parser Route Disambiguate Engine Location ambiguity Cambridge: Cambridge, MA or Cambridge, UK Map Renderer

  12. Thank you

More Related