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Classification of Semantic Relations in Noun Compounds using MeSH

Marti Hearst, Barbara RosarioSIMS, UC Berkeley

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LINDI Project Synopsis

Goal: Extract semantics from textMethod: statistical corpus analysisFocus: BioMedical text Interesting inferences (Swanson)Rich lexical resourcesDifficult NLP problems

Noun Compounds

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Noun Compounds (NCs)

Any sequence of nouns that itself functions as a noun asthma hospitalizations asthma hospitalization rates bone marrow aspiration needle health care personnel hand wash

Technical text is rich with NCs Open-labeled long-term study of the subcutaneous sumatriptan efficacy and tolerability in acute migraine treatment.

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NCs: 3 computational tasks

(Lauer & Dras ’94)IdentificationSyntactic analysis (attachments)

Baseline headache frequency Tension headache patient

Semantic analysis Headache treatment treatment for

headache Corticosteroid treatment treatment that uses

corticosteroid

[ ][ ][ ][ ]

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NC Semantic Relations

Linguistic theories regarding the nature of the relations between constituents in NCs all conflict. J. Levi ‘78P. Downing ’77B. Warren ‘78

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NC Semantic relations38 Relations found by iterative refinement based on 2245 NCsGoals:More specific than case rolesGeneral enough to aid coverageAllow for domain-specific relations

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Semantic relationsExamples

Frequency/time of influenza season, headache interval

Measure of relief rate, asthma mortality, hospital survival

Instrument aciclovir therapy, laser irradiation, aerosol treatment

“Purpose” headache drugs, voice therapy, influenza treatment

Defect hormone deficiency, csf fistulas, gene mutation

Inhibitor Adrenoreceptor blockers, influenza prevention

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Multi-class Assignment

Some NCs can be describe by more than one semantic relationships

eyelid abnormalities : location and defectfood allergy: cause and activator cell growth: change and activitytumor regression:change and

ending/reduction

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Extraction of NCs

1. Titles and abstracts from Medline (medical bibliographic database)

2. Part of Speech Tagger3. Extraction of sequences of units

tagged as nouns4. Collection of 2245 NCs with 2

nouns

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Models

Lexical (words) headache pain

Class based model using MeSH descriptors for levels of descriptions MeSH 2: C.23 G.11

MeSH 3: C23.888 G11.561

MeSH 4: C23.888.592 G11.561.796

MeSH 5: C23.888.592 G11.561.796

MeSH 6: C23.888.592.612 G11.561.796 .444

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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]

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MeSH Tree Structures 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] (….)

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Mapping Nouns to MeSH Concepts

headache recurrence C23.888.592.612.441 C23.550.291.937

headache pain C23.888.592.612.441 G11.561.796.444

breast cancer cells A01.236 C04 A11

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Levels of Descriptionheadache pain (C23.888.592.612.441 G11.561.796.444)

Only Tree: C G C(Diseases) G (Biological Sciences)

Level 1 : C 23 G 11 C 23 (Diseases: Pathological Conditions) G 11 (Biological Sciences: Musculoskeletal, Neural, and Ocular Physiology)

Level 2 : C 23 888 G 11 561 C 23.888 (Diseases:Pathological Conditions: Signs and symptoms) G 11.561 (Biological Sciences: Musculoskeletal, Neural, and Ocular Physiology:Nervous

System Physiology)

Level 3 : C 23 888 592 G 11 561 796 C 23.888.592 (Diseases :Pathological Conditions: Signs and symptoms: Neurologic

Manifestations) G 11.561.796 (Biological Sciences: Musculoskeletal, Neural, and Ocular

Physiology:Nervous System Physiology:Sensation)

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Classification Task & Method

Multi-class (18) classification problem

Multi layer Neural Networks to classify across all relations simultaneously.

Evaluation: distinguish between Seen: NCs where 1 or 2 words appeared in the

training set Unseen: NCs in which neither word appeared in

the training set

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Accuracy for 18-way Classification

Training

855 NCs

(50%)

Testing:

805 NCs

(75 unseen)

Correct answer in first two (71%-73%)

Correct answer ranked first (61%-62%)

Correct answer in first three (76%-78%)

Baseline (guessing most frequent class)

Lexical

MeSH

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Accuracies for 18-way classification: generalization on unseen NCs

Training:

73 NCs

(5%)

Testing:

1587 NCs

(810 unseen)

(95%)

MeSH

Lexical

MeSH on

unseen

Lexical on

unseen

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Accuracies by Unseen Noun

Training:

73 NCs

(5%)

Testing:

1587 NCs

(810 unseen)

(95%)

Case 1: first N unseen

Case 2: second N unseen

Case 3: both N seen

Case 4: neither N seen

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Accuracy for each relation

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Accuracy for sample relations

Produces (genetic)

Ex. Test Set:thymidine alleletumor dna csf mrna acetylase gene virion rna (…)

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Accuracy for sample relations

Frequency/time of

Test Set:disease recurrenceheadache recurrenceenterovirus seasoninfluenza seasonmosquito seasonpollen seasondisease stagetranscription stagedrive timeinjection timeischemia timetravel time

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Accuracy for sample relations

Purpose

Test Set:varicella vaccine tb vaccines poliovirus vaccine influenza vaccinationinfluenza immunizationabscess drainage acne therapy asthma therapy asthma treatment carcinogen treatment disease treatment hiv treatment

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Related work

Finin (1980) Detailed AI analysis, hand-coded

Rindflesch et al. (2000) Hand-coded rule base to extract certain

types of assertions

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Related workVanderwende (1994) automatically extracts semantic information from an on-line

dictionary manipulates a set of handwritten rules 13 classes 52% accuracy

Lapata (2000) classifies nominalizations into subject/object binary distinction 80% accuracy

Lauer (1995): probabilistic model 8 classes 47% accuracy

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Related workPrepositional Phrase Attachment The problem

Eat spaghetti with a fork Eat spaghetti with sauce V N1 P N2

Attachment/association, not semantics Approaches

Word occurrences (Hindle & Rooth ’93) Using a lexical hierarchy

Conceptual association (Resnik ’93, Resnik & Hearst ’93) Transformation-based (Brill & Resnik ’94) MDL to find optimal tree cut (Li & Abe ’98)

Lindi: use ML techniques to determine appropriate level of lexical hierarchy, classify into semantic relations

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ConclusionsA simple method for assigning semantic relations to noun compounds Does not require complex hand-coded rules Does make use of existing lexical resources

High accuracy levels for an 18-way class assignment Small training set gets ~60% accuracy on

mixed seen and unseen words Tiny training set (73 NCs) gets ~40%

accuracy on entirely unseen words Off-the-shelf, unoptimized ML algorithms

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Future work

Analysis of cases where it doesn’t workNC with > 2 termsHow to generalize patterns found for noun compounds to other syntactic structures? How can we best formally represent semantics?How can we deal with non medical words? Should we use other ontologies (e.g.,WordNet)?

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Using Relations

Eventual plan: combine relations with constituents’ ontology membershipsExamples

Instrument_2 (biopsy,needle) -> Instrument_2(Diagnostic, Tool)

Procedure(brain,biopsy) -> Procedure(Anatomical-Element, Diagnostic)

Procedure(tumor, marker) -> Procedure(Disease-element, Indicator)