Learning to Extract Relations from the Web using Minimal Supervision

1. Learning to Extract Relations from the Webusing Minimal Supervision Razvan C. Bunescu Raymond J. Mooney Machine Learning Group Department of Computer Sciences University of Texas at Austin Machine Learning Group Department of Computer Sciences University of Texas at Austin razvan@cs.utexas.edu mooney@cs.utexas.com

2. Introduction: Relation Extraction • People are often interested in finding relations between entities: • What proteins interact with IRAK1? • Which companies were acquired by Google? • In which city was Mozart born? • Relation Extraction (RE) is the task of automatically locating predefined types of relations in text documents.

3. Introduction: Relation Extraction • Relation Examples: • Protein Interactions: • Company Acquisitions: • People Birthplaces: • The phosphorylation of Pellino2 by activated IRAK1 could trigger the translocation of IRAKs from complex I to II. • Search engine giant Google has bought video-sharing website YouTube in a controversial $1.6 billion deal. • Wolfgang Amadeus Mozart was born to Leopold and Ana Maria Mozart, in the front room of Getreidegasse 9 in Salzburg.

4. Motivation: Minimal Supervision • Developing an RE system usually requires a significant amount of human effort: • Extraction patterns designed by a human expert [Blaschke et al., 2002]. • Extraction patterns learned from a corpus of manually annotated examples [Zelenko et al., 2003; Culotta and Sorensen, 2004]. • A different RE approach: • Extraction patterns learned from weak supervision derived from a significantly reduced amount of human supervision.

5. Relation Extraction with Minimal Supervision • Human supervision  a handful of pairs of entitiesknown to exhibit (+) or not exhibit (–) a particular relation. • Weak supervision  bags of sentencescontaining the pairs, automatically extracted from a very large corpus. • Use bags of sentences in a Multiple Instance Learning framework [Dietterich et al., 1997] to train a relation extraction model.

6. Types of Supervision for RE • Single Instance Learning (SIL): • A corpus of positive and negative sentence examples, with the two entity names annotated. • A sentence example is positive iff it explicitly asserts the target relationship between the two annotated entities. • Multiple Instance Learning (MIL): • A corpus of positive and negative bags of sentences. • A bag is positive iff it contains at least one positive sentence example.

7. RE from Web with Minimal Supervision Example pairs of named entities for RCorporate Acquisitions.

8. Minimal Supervision: Positive bags Use a search engine to extract bags of sentences containing both entities in a pair.

9. Minimal Supervision: Positive bags Use a search engine to extract bags of sentences containing both entities in a pair.

10. Minimal Supervision: Positive bags Use a search engine to extract bags of sentences containing both entities in a pair.

11. Minimal Supervision: Negative Bags Use a search engine to extract bags of sentences containing both entities in a pair.

12. Minimal Supervision: Negative Bags Use a search engine to extract bags of sentences containing both entities in a pair.

13. MIL Background: Domains • Originally introduced to solve a Drug Activity prediction problem in biochemistry [Dietterich et al., 1997] • Each molecule has a limited set of low energy conformations  bags of 3D conformations. • A bag is positive is at least one of the conformations binds to a predefined target. • MUSK dataset [Dietterich et al., 1997] • A bag is positive if the molecule smells “musky”. • Content Based Image Retrieval [Zhang et al., 2002] • Text categorization [Andrews et al., 03], [Ray et al., 05].

14. MIL Background: Algorithms • Axis Parallel Rectangles [Dietterich, 1997] • Diverse Density [Maron, 1998] • Multiple Instance Logistic Regression [Ray & Craven, 05] • Multi-Instance SVM kernels of [Gartner et al., 2002] • Normalized Set Kernel. • Statistic Kernel.

15. MIL for Relation Extraction • Focus on SVM approaches • Through kernels, can work efficiently with instances that implicitly belong to a high-dimensional feature spaces. • Can reuse existing relation extraction kernels. • Multi-Instance kernels of [Gartner et al., 2002] not appropriate when very few bags: • Bags (not instances) are considered as training examples. • The number of SVs is upper bounded by the number of bags • Very few bags  very few SVs  insufficient capacity.

16. MIL for Relation Extraction • A simple approach to MIL is to transform it into a standard supervised learning problem: • Apply the bag label to all instances inside the bag. • Train a standard supervised algorithm on the transformed dataset. • Despite class noise, obtains competitive results [Ray & Craven, 05]

17. MIL for Relation Extraction • A simple approach to MIL is to transform it into a standard supervised learning problem: • Apply the bag label to all instances inside the bag. • Train a standard supervised algorithm on the transformed dataset. • Despite class noise, obtains competitive results [Ray & Craven, 05]

18. SVM Framework with MIL Supervision minimize: subject to:

19. SVM Framework with MIL Supervision minimize: subject to: Regularization term

20. SVM Framework with MIL Supervision minimize: subject to: Error on positive bags

21. SVM Framework with MIL Supervision minimize: subject to: Error on negative bags

22. SVM Framework with MIL Supervision minimize: subject to: • cp, cn > 0, cp+ cn = 1, controls the relative influence that false negative vs. false positives have on the objective function. • want cp < 0.5 (penalize false negatives less than false positives); used cp = 0.1

23. SVM Framework with MIL Supervision minimize: subject to: • Dual formulation  kernel between bag instances K(x1,x2)  (x1)(x2). • Use SSK  a subsequence kernel customized for relation extraction. [Bunescu & Mooney, 2005]

24. The Subsequence Kernel for Relation Extraction [Bunescu & Mooney, 2005]. • Implicit features are sequences of words anchored at the two entity names. • s  a word sequence e1 … bought … e2 … billion … deal. • x  an example sentence, containing s as a subsequence Google has bought video-sharing website YouTube in a controversial $1.6 billion deal. g1 1 g2  3 g3  4 g4  0 • s(x)  the value of features in example x

25. The Subsequence Kernel for Relation Extraction [Bunescu & Mooney, 2005]. • K(x1,x2)  (x1)(x2)  the number of common “anchored” subsequences between x1 and x2, weighted by their total gap. • Many relations require at least one content word  modify kernel to optionally ignore sequences formed exclusively of stop words and punctuation signs. • Kernel is computed efficiently by a generalized version of the dynamic programming procedure from [Lodhi et al., 2002].

26. Two Types of Bias • The MIL approach to RE differs from other MIL problems in two respects: • The training dataset contains very few bags. • The bags can be very large. • These properties lead to two types of bias: • [Type I] Combinations of words that are correlated to the two relation arguments are given too much weight in the learned model. • [Type II] Words specific to a particular relation instance are given too much weight.

27. Type I Bias • Overweighted Patterns: • search … e1 … video … e2 • … e1 … video … e2 • e1 … search … e2 • e1 … search … e2 … video

28. Type II Bias • Overweighted Patterns: • … e1 … for … e2 … October • … e1 … has … e2 … October

29. A Solution for Type I Bias • Use the SSK approach, with new feature weight: • Modify subsequence kernel computations to use word weights (w). • Want small (w) for words w correlated with either of the two relation arguments.

30. A Solution for Type I Bias: Word Weights Use a formula for word weights (w) that discounts the effect of correlations of w with either of the two arguments a1 and a2.

31. A Solution for Type I Bias: Word Weights The # of sentences in bag X.

32. A Solution for Type I Bias: Word Weights The # of sentences in bag X that contain word w.

33. A Solution for Type I Bias: Word Weights The probability that the word w appears in a sentence due only to the presence of X.a1 or X.a2, assuming X.a1 and X.a2 are independent causes for w. • P(w|a) is the probability that w appears in a sentence due to the presence of a. • Estimate P(w|a) using counts from a separate bag of sentences containing a.

34. MIL Relation Extraction Datasets • Given two arguments a1 and a2, submit query string “a1 * * * * * * * a2” to Google. • Download the resulting documents (less than 1000). • Split text into sentences and tokenize using the OpenNLP package. • Keep only sentences containing both a1 and a2. • Replace closest occurrences of a1 and a2 with generic tags e1 and e2 .

35. MIL Relation Extraction Datasets Corporate Acquisitions Dataset Training Pairs Testing Pairs manually labeled all bag sentences

36. MIL Relation Extraction Datasets PersonBirthplace Dataset Training Pairs Testing Pairs manually labeled all bag sentences

37. Experimental Results: Systems • [SSK-MIL] MIL formulation using the original SSK. • [SSK-T1] MIL formulation with the SSK modified to use word weights in order to reduce Type I bias. • [BW-MIL] MIL formulation using a bag-of-words kernel. • [SSK-SIL] SIL formulation using the original subsequence kernel: • Use manually labeled instances from the test bags. • Train on instances from one positive bag and one negative bag, test on instances from the other two bags. • Average results over all four combinations.

38. Experimental Results: Evaluation • Plot Precisionvs.Recall(PR) graphs: • vary a threshold on the extraction confidence. • Report Area Under PR Curve (AUC).

39. Company Acquisitions

40. Person–Birthplace

41. Experimental Results: AUC • SSK-T1 is significantly more accurate than SSK-MIL. • SSK-T1 is competitive with SSK-SIL, however: • SSK-T1 supervision  only 6 pairs (4 positive). • SSK-SIL average supervision: • ~500 manually labeled sentences (78 positive) for Acquisitions. • ~300 manually labeled sentences (22 positive) for Birthplaces.

42. Applications & Extensions • A “Google Sets” system for relation extraction • Ideally, the user provides only positive pairs. • Likely negative examples are created by pairing the argument entity with other named entities in the same sentence. • Any pair of entities different from the relation pair is likely to be negative  implicit negative evidence. Input Output

43. Future Work • Investigate methods for reducing Type II bias. • Experiment with other, more sophisticated MIL algorithms. • Explore the effect of Type I and Type II bias when using dependency information in the relation extraction kernel.

44. Conclusion • Presented a new approach to Relation Extraction, trained using only a handful of pairs of entities known to exhibit or not exhibit the target relationship. • Extended an existing subsequence kernel to resolve problems caused by the minimal supervision provided. • The new MIL approach is competitive with its SIL counterpart that uses significantly more human supervision.