1. 1 Noun compounds (NCs) Any sequence of nouns that itself functions as a noun asthma hospitalizations asthma hospitalization rates 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.

2. 2 NCs: 3 computational tasks Identification Syntactic analysis (attachments) [Baseline [headache frequency]] [[Tension headache] patient] Semantic analysis Headache treatment treatment for headache Corticosteroid treatment treatment that uses corticosteroid

3. 3 Two approaches Treat it as a classification problem (and use a machine learning algorithm) Linguistically motivated: consider the “semantics” of the nouns which will determine the relations between them

4. 4 First approach Extraction of NCs from titles and abstracts of Medline Part-of-Speech Tagger Extraction of sequences of units tagged as nouns Collection of 2245 NCs with 2 nouns A manual annotation of the NCs found 38 semantic relations Collection of labeled NCs and a set of semantic relations

5. 5 Semantic relations 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, hiv medications, influenza treatment Defect hormone deficiency, csf fistulas, gene mutation Inhibitor Adrenoreceptor blockers, influenza prevention

6. 6 Semantic relations Cause Asthma hospitalization, aids death Change Papilloma growth, disease development Activity/Physical Process Bile delivery, virus reproduction Person Afflicted Aids patients, headache group ….

7. 7 Features Lexical (words) MeSH descriptors

8. 8 Classification method and results Multi-class (18) classification problem Multi layer Neural Networks to classify across all relations simultaneously. Results FeaturesAccuracy Words62% MeSH61% Baselines Guessing5% Most frequent relation31% Vanderwende94 (13 classes)52% Lapata00 (binary)80%

9. 9 Second approach Linguistic Motivation Head noun has argument structure Meaning of the head noun determines what kinds of things can be done to it, what it is made of, what it is a part of…

10. 10 Linguistic Motivation Material + Cutlery  Made of steel knife, plastic fork, wooden spoon Food + Cutlery  Used on meat knife, dessert spoon, salad fork Profession + Cutlery  Used by chef's knife, butcher's knife

11. 11 Linguistic Motivation Hypothesis: A particular semantic relation holds between all 2-word NCs that can be categorized by a MeSH pair. Use the classes of MeSH to identify semantic relations

12. 12 Grouping the NCs A02 C04 (Musculoskeletal System, Neoplasms) skull tumors, bone cysts, bone metastases, skull osteosarcoma… B06 B06 (Plants, Plants) eucalyptus trees, apple fruits, rice grains, potato plants A01 M01 (Body region, Person) shoulder patient, eye physician, eye donor Too different: need to be more specific: go down the hierarchy A01 M01.643 ( Body Regions, Patients) shoulder patient C04 M01.526 (Body Regions, Occupational Groups) eye physician, chest physicians

13. 13 Classification Decisions + Relations A02 C04  Location of Disease B06 B06  Kind of Plants C04 M01 C04 M01.643  Person afflicted by Disease C04 M01.526  Person who treats Disease A01 H01 A01 H01.770 A01 H01.671 A01 H01.671.538 A01 H01.671.868 A01 M01 A01 M01.643  Person afflicted by Disease A01 M01.526  Specialist of A01 M01.898  Donor of

14. 14 Evaluation Accuracy: Anatomy: 91% accurate Natural Science: 79% Neoplasm: 100% Total Accuracy : 90.8%

15. 15 Conclusion of NCs Problem of assigning semantic relations to two-word technical NCs Important problem: many NCs in technical text Especially difficult for the lack of syntactic clues State-of-the-art results One of very few working systems to tackle this task for NCs