1. Determining and Mapping Locations of Study in Scholarly Documents: A Spatial Representation and Visualization Tool for Information Discovery James Creel Katherine H. Weimer TCDL May 7, 2013

2. Geospatial Information Retrieval Challenges How to utilize locations represented in text? –20% of web queries have a geographic relation (Ahlers) Traditional catalog subjects and keywords do not suffice Location information is increasingly in demand (Reid) Ex. 1049 total ETDs in 2005 –300 included locations (< 30%) –130 contained international locations (> 10%)

3. What about a Visual Search? Searching collections with a map interface? –Visual representation of research –Enable serendipitous cross-disciplinary collaborations and networking –Enhance access to the collection

4. Map Prototype

5. 2011 – Geoparsing Work Begins 1.Overarching goal is to automate geocoding 2.Find toponyms in scholarly documents 3.Look up toponyms in a gazetteer 4.Disambiguate homonymous toponyms 5.Obtain geographic coordinates from gazetter 6.Encode coordinates in item surrogates for map-based view 7.Create map with link to original text

6. Desired Map Functionality 1.Base map: use Google Maps and other available interfaces 2.Cluster placemarks according to zoom level 3.List the displayed placemarks 4.Dropdown menu for countries and states in the US 5.Dropdown menu for departments grouped by college 1.Selection of multiple departments in more than one college 2.If selecting the college, then select all departments within the college 6.Search by author 7.Time range slider (by year) 8.Use the Web-friendly University Brand color palette

7. Geocoding with KML files The KML file with locations includes: –Author –Title –Academic department –Advisor –Degree level –Year –Place –Keywords –Url to document Info box displays: –Author –Title –Academic department –Degree level –Year –Place –Url to document

8. Beta Version of Map: Showing Google Street Maps

9. Clustering Mechanism

10. User clicks on Point of Interest  Title and Metadata Appear with Link to Text

11. Automated Process / Geoparser Geoparsing addresses two key problems: 1)Name extraction 2)Name disambiguation Document text Extracted names Disambiguated names Geospatial metadata

12. Geoparser: Comparable Models Edinburgh Geoparser –Grover, et. al. used OCR with historic records, provided the GeoCrossWalk gazetteer DIGMAP Geoparser –Martins, et al. used originally for DIGMAP digital library of historic maps

13. Geoparser: Setting DSpace 1.7 supports curation tasks –Custom Java programs Our instantiation: –Suggest New Metadata –Generate KML

14. Geoparser Workflow

15. Geoparser: Pre-Processing –DSpace filter-media script extracts plain-text from PDFs. –Suggest New Metadata curation task Partitions the document into sections using regular expressions Excludes sections containing non-topical toponyms (author-affiliation locations, conference locations, etc.)

16. Geoparser: Name Extraction ‘Named Entity Recognition’ or NER –Various open-source tools/training data Current version uses Apache OpenNLP or Stanford NER Classifies substrings of the text as names Toponym occurrences are recorded in context and counted

17. Name Disambiguation Requires reliable data- or knowledge-base We employ the Geonames dataset –Conglomeration of International gazetteers Includes GNIS (USGS) Several complimentary methods –Rule-based –Heuristic –Statistical

18. Heuristics: Overview Various heuristics can help indicate the probable referent of a given toponym Other heuristics can help pick out false positives from the classifiers Heuristics are based on context-clues in the text or on general observations about human discourse

19. Heuristics: Context-based One document, one sense Unambiguous extended names i.e. “Paris, France” Favor locations close to other mentioned locations Favor locations contained in other mentioned locations Favor locations of mentioned feature types

20. Heuristics: Generalized Favor higher-level administrative units (countries, states, cities) Favor locations of larger population

21. Heuristics: Application Heuristics - grouped into refinement iterations and then applied sequentially Resolve obvious cases first in order to provide better data for subsequent heuristics

22. Geoparser Evaluation Comparison of human annotations to geoparser output Precision/Recall of name extraction Accuracy of name disambiguation

23. Evaluator Workflow

24. Future Work Explore statistical disambiguation Explore relevance of toponyms to the subject matter Expand to TDL collections Expand to other digital collections or collection types, even the library catalog? Much more work to be done!

25. References Ahlers & Boll, “Location Based Web Search” in The Geospatial Web (London: Springer 2007) Apache OpenNLP. https://opennlp.apache.org/index.html DigMap. http://portal.digmap.edu/ Leidner, Jochen L. “Toponym Resolution in Text” (Univ. Edinburgh 2007) Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. “Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling” (ACL 2005) http://nlp.stanford.edu/~manning/papers/gibbscrf3.pdf Reid, James. “GeoXwalk – A Gazetteer Server and Service for UK Academia” (ECDL 2003)

26. Contact: –James Creel jcreel@library.tamu.edu –Kathy Weimer k-weimer@library.tamu.edu