1. Experiences with UIMA in NLP teaching and research Manuela Kunze, Dietmar Rösner University of Magdeburg C Knowledge Based Systems and Document Processing

2. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 2 Overview What is UIMA? First Experiments NLP Teaching Conclusion

3. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 3 UIMA: Unstructured Information Management Architecture a software architecture for developing and deploying unstructured information management (UIM) applications UIM application: a software system –analyse large volumes of unstructured information to discover, organize, and deliver relevant knowledge to the end user software architecture which specifies –component interfaces, data representations, … http://www.research.ibm.com/UIMA/

4. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 4 UIMA: Unstructured Information Management Architecture … interfaces to a collection of data items (e.g., documents) to be analyzed. Collection Readers return CASes that contain the documents to analyze, possibly along with additional metadata. … takes a CAS, analyzes its contents, and produces an enriched CAS. Analysis Engines can be recursively composed of other Analysis Engines (called an Aggregate Analysis Engine). Aggregates may also contain CAS Consumers. … may be used by a Collection Reader to populate a CAS from a document. An example of a CAS Initializer is an HTML parser that de-tags an HTML document and also inserts paragraph annotations (determined from tags in the original HTML) into the CAS. CAS: Common Analysis Structure CPE: Collecting Processing Manager … consume the enriched CAS that was produced by the sequence of Analysis Engines before it, and produce an application-specific data structure, such as a search engine index or database. [Ferucci et al.: Unstructured Information Management Architecture (UIMA): SDK User's Guide and Reference]

5. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 5 Analysis Engine (AE): –a component that analyzes artifacts (e.g. documents) and infers information about them –consists of two parts: Java classes (typically packaged as one or more JAR files) and AE descriptors (one or more XML files) –the configuration settings for the Analysis Engine as well as –a description of the AE’s input and output requirements. UIMA: Unstructured Information Management Architecture

6. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 6 UIMA: Unstructured Information Management Architecture analysis engine Annotator processing resources type system Annotation Interface define annotation type: name features (begin, end, …) describe analysis engine: annotator class input parameter output of annotations external resources interface resources linked to a type system uses define an annotator create Java XML

7. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 7 Aggregate Analysis Engine: –combine different analysis engine within one Analysis Engine UIMA: Unstructured Information Management Architecture [Ferucci et al.: Unstructured Information Management Architecture (UIMA): SDK User's Guide and Reference]

8. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 8 Overview Introduction First Experiments NLP Teaching Conclusion

9. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 9 First Experiments: UIMA vs. GATE base line: –2 persons, 2 systems, 1 corpus and 1 extraction task –skills/experiences of the persons: UIMAGATEEclipse/Java Person 1 Person 2  

10. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 10 Task of the Experiment process a corpus of websites –to detect and extract information relevant for tourists opening times of museum, prices of hotels,… corpus: –30 tourism web sites of Egypt –additional 20 web sites of Washington, New York, London output: –Prolog facts for a reasoner –Questions: Which museum is now open? …

11. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 11 Evaluation Topics/Points ease of getting acquainted with system?: –quality of docus: completeness, clarity, up-to-date, …? –tutorials, use cases, …? processing and linguistic resources? –lexica, Gazetteer lists, tools tools for resource maintenance and extension? –quality: selfexplanatory, robust, comfortable speed of processing? single document vs. large corpora? limitations, suggestions for improvement? support for im-/export of a variety of document formats?

12. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 12 Excerpts from the Corpus The Egyptian Museum is open the hours: 9am-5pm daily The Military Museum is open the hours: Summer: 8am- 5:30pm; winter: 8am-4:30pm Palace Museum is open the hours: 8am-5:30pm (summer) 8am-4:30pm (winter) 10am-2pm, 6pm-9pm Sat-Wed; 6pm-9pm Fri …

13. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 13 UIMA Application several annotators (like a pipeline) museum pattern time pattern interval of times restrictions museum information... *Fraunces Tavern Museum* 54 Pearl St. - 1-212-425-1778 Tuesday-Friday, 12pm?5pm; … regular expressions window covering two time intervals and a restriction window covering a museum and opening hours Prolog facts: museumopen('Fraunces Tavern Museum ', '2005-12-01T12:00:00','2005-12-01T17:00:00'). museumopen('Fraunces Tavern Museum ', '2005-12-02T12:00:00','2005-12-02T17:00:00'). museumopen('Fraunces Tavern Museum ', '2005-12-03T12:00:00','2005-12-03T17:00:00').

14. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 14 UIMA: Results information annotated in the documents: –names of museums, hotels –times, time intervals –time restrictions –prices, intervals of prices (hotel prices) –keywords for museum category –names of pharaohs (annotated with a correction of mispellings) information about hotel and museum are exported into Prolog facts and into a short textual summary –templates filled with the detected information hotels: Price information about Cosmopolitan Hotel : $157 museums: *** *Fraunces Tavern Museum* *** Open from 12:00:00 to 17:00:00; Restriction: Tuesday-Friday

15. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 15 UIMA vs. GATE: Conclusion no final judgement about: use GATE or UIMA –depends on your task task description expected results which processing resources are necessary your preferences for interface prefer the Eclispe environment (or other Java editors) prefer a comfortable GUI

16. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 16 UIMA vs. GATE: Conclusion GATE: tools available comfortable GUI UIMA: plain framework simplified definition of (complex) result structures simplified pre- and postprocessing of annotations both are extensible –e.g. for processing German documents

17. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 17 'German' Extension of Processing Resources XDOC document suite –tools for processing German documents –tools implemented in CommonLisp for UIMA –Java reimplementation of the tools –several analysis engines

18. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 18 XDOC in UIMA annotation of –part-of-speech (Morphix, heuristics) –semantic categories –named entities (vehicles, cities, …) a coarse approach for classification of PP –using maxent library

19. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 19 UIMA: Evaluation documentation? processing and linguistic resources? tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -good -illustrative examples (tutorial) -completeness: sometimes it is very shortly described -experiences with Eclipse and Java programming are advantageous -prior knowledge about Java and Eclipse is helpful

20. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 20 UIMA: Evaluation documentation? processing and linguistic resources? tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -annotators only from tutorial -sentence annotation -word annotation -date/time annotators -examples for using regular expressions etc. -external resources can be integrated: -lexical resources as external resources (text files) -existing processing resources -implementation of an interface is necessary

21. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 21 UIMA: Evaluation documentation? processing and linguistic resources? tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -specific Eclipse component editors or -simple text editors

22. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 22 UIMA: Evaluation documentation processing and linguistic resources tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -faster than GATE? -in CPE detailed information about processing time for each module

23. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 23 UIMA: Evaluation documentation processing and linguistic resources tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -Collection Reader -document(s) from a directory

24. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 24 UIMA: Evaluation documentation processing and linguistic resources tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? no limitations: –all is possible, but implementation or interfacing by user wish: –more processing and linguistic resources within the distribution

25. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 25 UIMA: Evaluation documentation processing and linguistic resources tools for resource maintenance and extension? speed of processing? single docs vs. large corpora? limitations, suggestions for improvement? im-/export of document formats? -import: CAS Initializer -export: CAS Consumer -transform annotations in any other format -export of -document + annotations -only annotations -required: Java application

26. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 26 Overview Introduction First Experiments NLP Teaching Conclusion

27. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 27 NLP Teaching course: Information Extraction aim of the course: to make our students acquainted with information extraction as basic NLP technology –UIMA, GATE students: computer science, data-knowledge engineering skills of the students: programming Java

28. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 28 NLP Teaching different corpora: –news about FIFA world cup 2006 in Germany, –description of drugs, –announcements of new books, … tasks for students –to develop different anaylsis engines and combine them for annotation of URLs, email addresses, name of players, results of games, … using regular expressions, external resources, maximum entropy models

29. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 29 NLP Teaching

30. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 30 UIMA: A Students View easy to handle Java programming (environment) problems of students: –to understand the dependencies between the several descriptors for teaching helpful (future work): –a 'comparator' of different solutions of students –which solution is the best, related to a 'master' solution

31. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 31 Overview Introduction First Experiments NLP Teaching Conclusion

32. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 32 Conclusion UIMA: –easy to learn and to handle –support the management of different annotations different processing resources –integration of external resources (processing resources as well lexical resources) –splitting of 'processing steps': reader, initalizer, analysis engine, consumer 'wish-list': –a kind of jape transducer interface to GATE's processing resources is available –'comparator' for evaluation of solutions

33. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 33

34. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 34 XDOC in UIMA

35. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 35 Introduction really?

36. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 36 Introduction first experiments with UIMA –processing tourism web sites, news about the FIFA world cup 2006 in Germany, … –integration of tools from the XDOC document suite using UIMA in a course on Information Extraction

37. Kunze, Rösner: Experiences with UIMA in NLP teaching and research 37 Introduction "IBM’s Unstructured Information Management Architecture (UIMA) is an architecture and software framework for creating, discovering, composing and deploying a broad range of multi-modal analysis capabilities and integrating them with search technologies." November 2005; Version 1.2.3 of UIMA is available