1. I256 Applied Natural Language Processing Fall 2009 Lecture 14 Information Extraction (2) Barbara Rosario

2. 2 Today Midterm evaluations Discuss schedule for next class Finish slides lecture 13 Information Extraction (2)

3. 3 Text Mining/Information Extraction Text: –Stress is associated with migraines –Stress can lead to loss of magnesium –Calcium channel blockers prevent some migraines –Magnesium is a natural calcium channel blocker 1: Extract semantic entities from text

4. 4 Text Mining Text: –Stress is associated with migraines –Stress can lead to loss of magnesium –Calcium channel blockers prevent some migraines –Magnesium is a natural calcium channel blocker StressMigraine Magnesium Calcium channel blockers 1: Extract semantic entities from text

5. 5 Text Mining (cont.) Text: –Stress is associated with migraines –Stress can lead to loss of magnesium –Calcium channel blockers prevent some migraines –Magnesium is a natural calcium channel blocker StressMigraine Magnesium Calcium channel blockers 2: Classify relations between entities Associated with Lead to lossPrevent Subtype-of (is a)

6. 6 Text Mining (cont.) Text: –Stress is associated with migraines –Stress can lead to loss of magnesium –Calcium channel blockers prevent some migraines –Magnesium is a natural calcium channel blocker StressMigraine Magnesium Calcium channel blockers 3: Do reasoning: find new correlations Associated with Lead to loss Prevent Subtype-of (is a)

7. 7 Text Mining (cont.) Text: –Stress is associated with migraines –Stress can lead to loss of magnesium –Calcium channel blockers prevent some migraines –Magnesium is a natural calcium channel blocker StressMigraine Magnesium Calcium channel blockers 4: Do reasoning: infer causality Associated with Lead to loss Prevent Subtype-of (is a) No prevention Deficiency of magnesium  migraine

8. 8 Death Receptors Signaling Survival Factors Signaling Ca ++ Signaling P53 pathway Caspase 12 Effecter Caspases (3,6,7) Caspase 9 Apaf 1 IAPs NFkB Mitochondria Cytochrome c Bax, Bak Apoptosis Bcl-2 like BH3 only Apoptosis Network Smac ER Stress Genotoxic Stress Initiator Caspases (8, 10) AIF Lost of Attachment Cell Cycle stress, etc

9. 9 Project at UC Berkeley The network nodes are deduced from reading and processing of experimental knowledge by experts. Every month >1000 apoptosis papers are published. We need to keep track of ALL the information in order to understand the system better. Ultimate Goal: Produce models capable to predict system behavior, identify critical control points and propose critical experiments to extend current knowledge. 2005 Project: Develop an automatic literature analysis tool –Zhang and Arkin, UC Berkeley

10. 10 To convert free text into structured format (manually!)

11. 11 To convert free text into structured format (manually!)

12. Problem: Which relations hold between 2 entities? TreatmentDisease Cure? Prevent? Side Effect?

13. 13 Hepatitis Examples Cure –These results suggest that con A-induced hepatitis was ameliorated by pretreatment with TJ-135. Prevent –A two-dose combined hepatitis A and B vaccine would facilitate immunization programs Vague –Effect of interferon on hepatitis B

14. 14 Two tasks Relationship Extraction: –Identify the several semantic relations that can occur between the entities disease and treatment in bioscience text Often the different relationships are determined by the entities involved –Location of: LOCATION and ORGANIZATION Here different relations between the same entities Entity extraction: –Related problem: identify such entities

15. 15 The Approach Data: MEDLINE abstracts and titles Graphical models –Combine in one framework both relation and entity extraction –Both static and dynamic models Simple discriminative approach: –Neural network Lexical, syntactic and semantic features

16. 16 Several DIFFERENT Relations between the Same Types of Entities Thus differs from the problem statement of other work on relations Many find one relation which holds between two entities (many based on ACE) –Agichtein and Gravano (2000), lexical patterns for location of –Zelenko et al. (2002) SVM for person affiliation and organization-location –Hasegawa et al. (ACL 2004) Person-Organization -> President “relation” –Craven (1999, 2001) HMM for subcellular-location and disorder-association Doesn’t identify the actual relation

17. 17 Related work: Bioscience Many hand-built rules –Feldman et al. (2002), –Friedman et al. (2001) – Pustejovsky et al. (2002) –Saric et al.; this conference

18. 18 Data and Relations MEDLINE, abstracts and titles 3662 sentences labeled –Relevant: 1724 –Irrelevant: 1771 e.g., “Patients were followed up for 6 months” 2 types of Entities, many instances –treatment and disease 7 Relationships between these entities The labeled data is available at http://biotext.berkeley.edu

19. 19 Labeling

20. 20 Inter-annotators agreement F-measures between the 2 annotations was 81% (an upper limit for the system performance)

21. 21 Annotators’ disagreement

22. 22 Semantic Relationships 810: Cure –Intravenous immune globulin for recurrent spontaneous abortion 616: Only Disease –Social ties and susceptibility to the common cold 166: Only Treatment –Flucticasone propionate is safe in recommended doses 63: Prevent –Statins for prevention of stroke

23. 23 Semantic Relationships 36: Vague –Phenylbutazone and leukemia 29: Side Effect –Malignant mesodermal mixed tumor of the uterus following irradiation 4: Does NOT cure –Evidence for double resistance to permethrin and malathion in head lice

24. 24 Preprocessing Sentence splitter Tokenizer (Penn tree bank) Brill’s POS Collins parser

25. 25 Preprocessing

26. 26 Preprocessing Chunking Semantic tagging with MeSH: map the words into MeSH terms.

27. 27 MeSH MeSH Tree Structures 1. Anatomy [A] 2. Organisms [B] 3. Diseases [C] 4. Chemicals and Drugs [D] 5. Analytical, Diagnostic and Therapeutic Techniques and Equipment [E] 6. Psychiatry and Psychology [F] 7. Biological Sciences [G] 8. Physical Sciences [H] 9. Anthropology, Education, Sociology and Social Phenomena [I] 10. Technology and Food and Beverages [J] 11. Humanities [K] 12. Information Science [L] 13. Persons [M] 14. Health Care [N] 15. Geographic Locations [Z]

28. 28 MeSH 1. Anatomy [A] Body Regions [A01] + Musculoskeletal System [A02] Digestive System [A03] + Respiratory System [A04] + Urogenital System [A05] + Endocrine System [A06] + Cardiovascular System [A07] + Nervous System [A08] + Sense Organs [A09] + Tissues [A10] + Cells [A11] + Fluids and Secretions [A12] + Animal Structures [A13] + Stomatognathic System [A14] ( …..) Body Regions [A01] –Abdomen [A01.047] Groin [A01.047.365] Inguinal Canal [A01.047.412] Peritoneum [A01.047.596] + Umbilicus [A01.047.849] –Axilla [A01.133] –Back [A01.176] + –Breast [A01.236] + –Buttocks [A01.258] –Extremities [A01.378] + –Head [A01.456] + –Neck [A01.598] –( ….)

29. 29 Preprocessing

30. 30 Features Word Part of speech Phrase constituent (the phrase type from the shallow parse: NP< PP..) Belongs to the same chunk as previous word? Orthographic features –Is number? –Only part is number –Is negation –First letter is capital –All capital letters –All non word character –Contains non word character MeSH (semantic features) Extract these features with Python

31. 31 Models 2 static generative models 3 dynamic generative models –Smoothing: absolute discounting 1 discriminative model (neural networks) These models in Matlab

32. 32 Architecture Get text (Python) Annotate Preprocessing, extract features and transform features into numbers (Python) Algorithms (Matlab) Process output (Python) numerical features prediction

33. 33 Static Graphical Models –S1: observations dependent on Role but independent from Relation given roles –S2: observations dependent on both Relation and Role S1S2

34. 34 Dynamic Graphical Models D1, D2 as in S1, S2 D3: only one observation per state is dependent on both the relation and the role D1 D2 D3

35. 35 Graphical Models Relation node: –Semantic relation (cure, prevent, none..) expressed in the sentence

36. 36 Graphical Models Role nodes: –3 choices: treatment, disease, or none

37. 37 Graphical Models Feature nodes (observed): –word, POS, MeSH…

38. 38 Graphical Models Different dependencies between the features and the relation nodes D3 D1 S1 D2 S2

39. 39 Graphical Models For Dynamic Model D1: –Joint probability distribution over relation, roles and features nodes –Parameters estimated with maximum likelihood and absolute discounting smoothing

40. 40 Neural Networks Feed-forward network (MATLAB) –Training with conjugate gradient descent –One hidden layer (hyperbolic tangent function) –Logistic sigmoid function for the output layer representing the relationships Same features Discriminative approach

41. 41 Relation extraction Results in terms of classification accuracy (with irrelevant sentences) 2 cases: –Roles hidden –Roles given Graphical models NN: simple classification problem

42. 42 Relation classification: Results InputBaseStaticDynamicNN S1S2D1D2D3 only feature s 46.7 51.150.268.974.974.679.6 feature s + roles 50.6 56.154.891.682.082.296.6 Accuracies on the relation classification task

43. 43 Relation classification: Confusion Matrix Computed for the model D2, “only features”

44. 44 Role extraction Results in terms of F-measure Graphical models –Junction tree algorithm (BNT) –Relation hidden and marginalized over NN –Couldn’t run it (features vectors too large) (Graphical models can do role extraction and relationship classification simultaneously)

45. 45 Evaluation POS: Possible. The total number of truth values. POS = COR + INC + MIS ACT: Actual. The total number of predictions. ACT = COR + INC + SPU REC: Recall. A measure of how many of the truth values were produced: REC = COR / POS PRE: Precision. A measure of how many of the predictions are actually in the truth: PRE = COR / ACT

46. 46 Evaluation Alignment: PredictionTrue value From these we derive F-measures

47. 47 Role Extraction: Results StaticDynamic S1S2D1D2D3 0.60.620.670.710.69 F-measures

48. 48 Features impact: Role Extraction Most important features: 1)Word, 2)MeSH Models D1 D2 All features 0.67 0.71 No word 0.58 0.61 -13.4% -14.1% No MeSH 0.63 0.65 -5.9% -8.4%

49. 49 Most important features: Roles Accuracy: D1 D2 NN All feat. + roles 91.6 82.0 96.9 All feat. – roles 68.9 74.9 79.6 -24.7% -8.7% -17.8% All feat. + roles – Word 91.6 79.8 96.4 0% -2.8% -0.5% All feat. + roles – MeSH 91.6 84.6 97.3 0% 3.1% 0.4% Features impact: Relation classification

50. 50 Features impact: Relation classification Most realistic case: Roles not known Most important features: 1) Mesh 2) Word for D1 and NN (but vice versa for D2) Accuracy: D1 D2 NN All feat. – roles 68.9 74.9 79.6 All feat. - roles – Word 66.7 66.1 76.2 -3.3% -11.8% -4.3% All feat. - roles – MeSH 62.7 72.5 74.1 -9.1% -3.2% -6.9%

51. 51 Conclusions Classification of subtle semantic relations in bioscience text –Discriminative model (neural network) achieves high classification accuracy –Graphical models for the simultaneous extraction of entities and relationships –Importance of lexical hierarchy