Insights into Analogy Completion from the Biomedical Domain
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1 Insights into Analogy Completion from the Biomedical Domain Denis Newman-Griffis, Albert Lai, Eric Fosler-Lussier The Ohio State University National Institutes of Health, Clinical Center Washington University in St. Louis BioNLP 2017 August 4, 2017
2 TL;DR Increasing work on training embedding-based models for biomedical applications, but not many resources to evaluate on.
3 TL;DR Increasing work on training embedding-based models for biomedical applications, but not many resources to evaluate on. Analogies have been highly useful in the general domain, so we built an analogy dataset for BioNLP
4 TL;DR Increasing work on training embedding-based models for biomedical applications, but not many resources to evaluate on. Analogies have been highly useful in the general domain, so we built an analogy dataset for BioNLP. 1 Findings: Current embeddings are good at direct chemical/biological relationships, not so good at clinical semantics. Changes need to be made to the standard analogy methods to reflect the complexity of real data. 1
5 Outline The analogy completion task BMASS How can we make analogies more realistic? Findings and challenges on our dataset
6 The analogy completion task London : England :: Paris : France
7 The analogy completion task London : England :: Paris :
8 The analogy completion task London : England :: Paris : d = argmax d V ( cos(d, Eng Lndn + Paris) ) England London Paris??
9 The analogy completion task London : England :: Paris : ( ) d = argmax d V cos(d, Eng Lndn + Paris) England?? London Paris France Switzerland Italy urology swimming purple Latvia
10 The analogy completion task London : England :: Paris : ( ) d = argmax d V cos(d, Eng Lndn + Paris) England?? London Paris Italy Switzerland France urology swimming purple Latvia
11 BMASS - BioMedical Analogic Similarity Set Unified Medical Language System (UMLS)
12 BMASS - BioMedical Analogic Similarity Set Unified Medical Language System (UMLS) Normalized concepts C common cold cold acute rhinitis
13 BMASS - BioMedical Analogic Similarity Set Unified Medical Language System (UMLS) Normalized concepts Relation triples C common cold cold acute rhinitis subject relation object C RO:has-finding-site C
14 BMASS - BioMedical Analogic Similarity Set ID Name Amb Lab/Rx L1 form-of 1.0 L2 has-lab-number 1.1 L3 has-tradename 1.5 L4 tradename-of 1.3 L5 associated-substance 1.6 L6 has-free-acid-or-baseform 1.0 L7 has-salt-form 1.1 L8 measured-component-of 1.3 Hierarchical H1 refers-to 1.0 H2 same-type 10.4 Morphological M1 adjectival-form-of 1.1 M2 noun-form-of 1.0 C2 1.5 ID Name Amb Clinical C1 associated-with-malfunction-of- 2.6 gene-product gene-product-malfunctionassociated-with-disease C3 causative-agent-of 4.6 C4 has-causative-agent 2.0 C5 has-finding-site 1.9 C6 associated-with 1.2 Anatomy A1 anatomic-structure-is-part-of 1.6 A2 anatomic-structure-has-part 5.4 A3 is-located-in 1.4 Biology B1 regulated-by 1.0 B2 regulates 1.0 B3 gene-encodes-product 1.1 B4 gene-product-encoded-by 2.4 Cross-product of 50 samples for each relation: 2,450 analogies for each relation 61,250 total analogies
15 This dataset represents real biomedical relationships...
16 This dataset represents real biomedical relationships... But it doesn t fit the standard paradigm!
17 Unassuming the standard assumptions 3 key assumptions in evaluation methodology: Single Answer Same Relationship(s) Informativity Each is violated in recent analogy datasets Google 2, BATS 3, Sem-Para 4 All are problematic in real-world data! 2 Mikolov et al Gladkova et al Köper et al. 2015
18 Unassuming the standard assumptions Single Answer Same Relationship Informativity The given analogy has only one correct target. Enforced by argmax over candidates for completing the analogy. If multiple analogies, must get at least one wrong. Problem cases flu : nausea :: fever : sweating weakness
19 Unassuming the standard assumptions Single Answer Same Relationship Informativity The given analogy has only one correct target. Enforced by argmax over candidates for completing the analogy. If multiple analogies, must get at least one wrong. Problem cases flu : nausea :: fever : sweating weakness Easy fix! Allow for multiple correct answers; also report on all of them, for fuller picture.
20 Unassuming the standard assumptions Single Answer Same Relationship Informativity All information relating exemplars a and b also relates query c to d. Enforced by treating full vector difference as relation of interest. Many relations have partial overlap with one another. Problem cases
21 Unassuming the standard assumptions Single Answer Same Relationship Informativity All information relating exemplars a and b also relates query c to d. Enforced by treating full vector difference as relation of interest. Many relations have partial overlap with one another. Problem cases brother : sister :: husband : wife MaleCounterpart SiblingOf MaleCounterpart MarriedTo
22 Unassuming the standard assumptions Single Answer Same Relationship Informativity All information relating exemplars a and b also relates query c to d. Enforced by treating full vector difference as relation of interest. Many relations have partial overlap with one another. Problem cases brother : sister :: husband : wife MaleCounterpart SiblingOf MaleCounterpart MarriedTo wrist pain : shoulder pain :: bipolar disorder : trazodone CoOccursWith CoOccursWith TreatmentFor
23 Unassuming the standard assumptions Single Answer Same Relationship Informativity The relationship between exemplars a and b is specific enough to suggest the correct target d for query c. Issue with very broad semantic or hierarchical relationships. Problem cases
24 Unassuming the standard assumptions Single Answer Same Relationship Informativity The relationship between exemplars a and b is specific enough to suggest the correct target d for query c. Issue with very broad semantic or hierarchical relationships. Problem cases Generally related (UMLS) socks : stockings :: Finns : Finnish language
25 Unassuming the standard assumptions Single Answer Same Relationship Informativity The relationship between exemplars a and b is specific enough to suggest the correct target d for query c. Issue with very broad semantic or hierarchical relationships. Problem cases Generally related (UMLS) socks : stockings :: Finns : Finnish language Fix during dataset generation Review samples from each relation to ensure they re properly determined.
26 Modifying the evaluation methodology a : b :: c : d = argmax d V ( cos(d, b a + c) ) Evaluating analogies under 3 settings:
27 Modifying the evaluation methodology a : b :: c : d = argmax d V ( cos(d, b a + c) ) Evaluating analogies under 3 settings: Single-Answer (SA) Single candidate target for each analogy selected as the only correct answer. flu : nausea :: fever : sweating weakness weakness temperature sweating nodule
28 Modifying the evaluation methodology a : b :: c : d = argmax d V ( cos(d, b a + c) ) Evaluating analogies under 3 settings: Single-Answer (SA) Single candidate target for each analogy selected as the only correct answer. Multi-Answer (MA) All candidate targets for each analogy are considered to be correct. flu : nausea :: fever : sweating weakness weakness temperature sweating nodule
29 Modifying the evaluation methodology a : b :: c : d = argmax d V ( cos(d, b a + c) ) Evaluating analogies under 3 settings: Single-Answer (SA) Single candidate target for each analogy selected as the only correct answer. Multi-Answer (MA) All candidate targets for each analogy are considered to be correct. All-Info (AI) Use all possible exemplar objects and all candidate targets. flu : nausea cough :: fever : sweating weakness b a = ( ) 1 nausea - flu 2 + cough - flu
30 Evaluation methodology Reporting 3 metrics over ranked candidates: Acc R Relaxed accuracy; correct if any valid answer is the top choice MAP Mean average precision MRR Mean reciprocal rank weakness temperature sweating nodule Acc R MAP 6 MRR 1.0
31 Overall results Results shown for Multi-Answer setting. Average performance is around 11% on all metrics, with all embeddings. High variability between relationships! Used 5 different sets of embeddings trained on PubMed.
32 MAP/MRR give a better picture MAP < Acc R indicates wider distribution of correct answers on L7 (has-salt-form) MAP > Acc R shows that even if top answer is wrong, correct answers aren t far down on C6 (associated-with), B2 (regulates)
33 MAP/MRR give a better picture MRR > Acc R shows that the best correct answer stays near the top on C4 (has-causative-agent) MRR Acc R reflects more consistent positioning of nearest correct answer on H1 (refers-to), C3 (causative-agent-of)
34 All-Info benefits vary Extra examples help on H2 (same-type), L5 (associated-substance), and C4 (has-causative-agent). But harm L1 (form-of) (4% absolute) and L6 (has-free-acid-or-base-form) (8% absolute)
35 Moving analogies forward Single-Answer and Informativity assumptions addressed, but not Same Relationship(s).
36 Moving analogies forward Single-Answer and Informativity assumptions addressed, but not Same Relationship(s). Drozd et al (2016) use a parametric logistic regression that can be used to learn affine subspaces.
37 Moving analogies forward Single-Answer and Informativity assumptions addressed, but not Same Relationship(s). Drozd et al (2016) use a parametric logistic regression that can be used to learn affine subspaces. Standard linear offset method does not work for real-world data! Our changes help, but overall performance is still low (as with other recent datasets). Use MAP and MRR! Analogies are useful! We need to find better ways to tackle this task.
38 Thank you! Dataset and source code at:
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