1. Annotation Free Information Extraction Chia-Hui Chang Department of Computer Science & Information Engineering National Central University chia@csie.ncu.edu.tw 10/4/2002

2. Introduction TEXT IE  AutoSlog-TS Semi IE  IEPAD

3. AutoSlog-TS: Automatically Generating Extraction Patterns from Untagged Text Ellen Riloff University of Utah AAAI96

4. AutoSlog-TS AutoSlog-TS is an extension of AutoSlog  It operates exhaustively by generating an extraction pattern for every noun phrase in the training corpus.  It then evaluates the extraction patterns by processing the corpus a second time and generating relevance statistics for each pattern. A more significant difference is that AutoSlog-TS allows multiple rules to fire if more than one matches the context.

5. AutoSlog-TS Concept

6. Relevance Rate Pr(relevant text | text contains pattern i ) = rel-freq i / total-freq i rel-freq i : the number of instances of pattern i that were activated in relevant texts. total-freq i : the total number of instances of pattern i that were activated in the training corpus. The motivation behind the conditional probability estimate is that domain-specific expressions will appear substantially more often in relevant texts than irrelevant texts.

7. Rank function Next, we use a rank function to rank the patterns in order of importance to the domain: relevance rate * log 2 (frequency) So, a person only needs to review the most highly ranked patterns.

8. Experimental Results Setup We evaluated AutoSlog and AutoSlog-TS by manually inspecting the performance of their dictionaries in the MUC-4 terrorism domain. We used the MUC-4 texts as input and the MUC-4 answer keys as the basis for judging “correct” output (MUC-4 Proceedings 1992). Training 32345 11225 210 1237 450 Extraction Patterns 1500,50% relevant 772 relevant Texts AutoSlog-TS: AutoSlog:

9. Testing To evaluate the two dictionaries, we chose 100 blind texts from the MUC-4 test set. (50 relevant texts and 50 irrelevant texts) We scored the output by assigning each extracted item to one of five categories: correct, mislabeled, duplicate, spurious, or missing.  Correct: If an item matched against the answer keys.  Mislabeled: If an item matched against the answer keys but was extracted as the wrong type of object.  Duplicate: If an item was referent to an item in the answer keys.  Spurious: If an item did not refer to any object in the answer keys.  Missing: Items in the answer keys that were not extracted

10. Experimental Results We scored three items: perpetrators, victims, and targets.

11. Experimental Results We calculated recall as correct / (correct + missing) Compute precision as: (correct + duplicate) / (correct + duplicate + mislabeled + spurious)

12. Behind the scenes In fact, we have reason to believe that AutoSlog-TS is ultimately capable of producing better recall than AutoSlog because it generates many good patterns that AutoSlog did not. AutoSlog-TS produced 158 patterns with a relevance rate ≧ 90% and frequency ≧ 5. Only 45 of these patterns were in the original AutoSlog dictionary. The higher precision demonstrated by AutoSlog-TS is probably a result of the relevance statistics.

13. Future Directions A potential problem with AutoSlog-TS is that there are undoubtedly many useful patterns buried deep in the ranked list, which cumulatively could have a substantial impact on performance. The precision of the extraction patterns could also be improved by adding semantic constraints and, in the long run, creating more complex extraction patterns.

14. IEPAD: Information Extraction based on Pattern Discovery Information Extraction based on Pattern Discovery C.H. Chang. National Central University WWW10

15. Semi-structured Information Extraction Information Extraction (IE)  Input: Html pages  Output: A set of records

16. Pattern Discovery based IE  Motivation Display of multiple records often forms a repeated pattern The occurrences of the pattern are spaced regularly and adjacently  Now the problem becomes... Find regular and adjacent repeats in a string

17. IEPAD Architecture Pattern Generator Extractor Extraction Results Html Page Patterns Pattern Viewer Extraction Rule Users Html Pages

18. The Pattern Generator Translator PAT tree construction Pattern validator Rule Composer HTML Page Token Translator PAT Tree Constructor Validator Rule Composer PAT trees and Maximal Repeats Advenced Patterns Extraction Rules A Token String

19. 1. Web Page Translation Encoding of HTML source  Rule 1: Each tag is encoded as a token  Rule 2: Any text between two tags are translated to a special token called TEXT (denoted by a underscore) HTML Example: Congo 242 Egypt 20 Encoded token string T( )T(_)T( )T( )T(_)T( )T( )

20. Various Encoding Schemes

21. 2. PAT Tree Construction PAT tree: binary suffix tree A Patricia tree constructed over all possible suffix strings of a text Example T( ) 000 T( )001 T( )010 T( )011 T( )100 T(_)110 000110001010110011100 T( )T(_)T( )T( )T(_)T( )T( )

22. The Constructed PAT Tree

23. Definition of Maximal Repeats Let  occurs in S in position p 1, p 2, p 3, …, p k  is left maximal if there exists at least one (i, j) pair such that S[p i -1]  S[p j -1]  is right maximal if there exists at least one (i, j) pair such that S[p i +|  |]  S[p j +|  |]  is a maximal repeat if it it both left maximal and right maximal

24. Finding Maximal Repeats Definition:  Let’s call character S[p i -1] the left character of suffix p i  A node is left diverse if at least two leaves in the ’s subtree have different left characters Lemma:  The path labels of an internal node in a PAT tree is a maximal repeat if and only if is left diverse

25. 3. Pattern Validator Suppose a maximal repeat  are ordered by its position such that suffix p 1 < p 2 < p 3 … < p k, where p i denotes the position of each suffix in the encoded token sequence. Characteristics of a Pattern  Regularity: Variance coefficient  Adjacency: Density

26. Pattern Validator (Cont.) Basic Screening For each maximal repeat , compute V(  ) and D(  ) a) check if the pattern’s variance: V(  ) < 0.5 b) check if the pattern’s density: 0.25 < D(  ) < 1.5 V(  )<0.5 0.25<D(  )<1.5 Yes No Discard Yes Pattern  No Discard Pattern 

27. 4. Rule Composer Occurrence partition  Flexible variance threshold control Multiple string alignment  Increase density of a pattern

28. Occurrence Partition Problem  Some patterns are divided into several blocks  Ex: Lycos, Excite with large regularityLycosExcite Solution  Clustering of the occurrences of such a pattern Clustering V(  )<0.1 No Discard  Check density Yes

29. Multiple String Alignment Problem  Patterns with density less than 1 can extract only part of the information Solution  Align k-1 substrings among the k occurrences  A natural generalization of alignment for two strings which can be solved in O(n*m) by dynamic programming where n and m are string lengths.

30. Multiple String Alignment (Cont.) Suppose “ adc ” is the discovered pattern for token string “ adcwbdadcxbadcxbdadcb ” If we have the following multiple alignment for strings ``adcwbd'', ``adcxb'' and ``adcxbd'': a d c w b d a d c x b - a d c x b d The extraction pattern can be generalized as “ adc[w|x]b[d|-] ”

31. Pattern Viewer Java-application based GUI Web based GUI  http://www.csie.ncu.edu.tw/~chia/WebIEPAD/

32. The Extractor Matching the pattern against the encoding token string  Knuth-Morris-Pratt’s algorithm  Boyer-Moore’s algorithm Alternatives in a rule  matching the longest pattern What are extracted?  The whole record

33. Experiment Setup Fourteen sources: search engines Performance measures  Number of patterns  Retrieval rate and Accuracy rate Parameters  Encoding scheme  Thresholds control

34. # of Patterns Discovered Using BlockLevel Encoding Average 117 maximal repeats in our test Web pages

35. Translation Average page length is 22.7KB

36. Accuracy and Retrieval Rate

37. Summary IEPAD: Information Extraction based on Pattern Discovery  Rule generator  The extractor  Pattern viewer Performance  97% retrieval rate and 94% accuracy rate

38. Problems Guarantee high retrieval rate instead of accuracy rate  Generalized rule can extract more than the desired data Only applicable when there are several records in a Web page, currently

39. References TEXT IE  Riloff, E. (1996) Automatically Generating Extraction Patterns from Untagged Text, (AAAI- 96), 1996, pp. 1044-1049.  Riloff, E. (1999) Information Extraction as a Stepping Stone toward Story Understanding, In Computational Models of Reading and Understanding, Ashwin Ram and Kenneth Moorman, eds., The MIT Press.

40. References Semi-structured IE  D.W. Embley, Y.S. Jiang, and W.-K. Ng, Record- Boundary Discovery in Web Documents, SIGMOD'99 ProceedingsRecord- Boundary Discovery in Web Documents  C.H. Chang. and S.C. Lui. IEPAD: Information Extraction based on Pattern Discovery, WWW10, pp. 681-688, May 2-6, 2001, Hong Kong.IEPAD: Information Extraction based on Pattern Discovery  B. Chidlovskii, J. Ragetli, and M. de Rijke, Automatic Wrapper Generation for Web Search Engines, The 1st Intern. Conf. on Web-Age Information Management (WAIM'2000), Shanghai, China, June 2000Automatic Wrapper Generation for Web Search Engines