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25

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R. Nakajo, S. Murata, H. Arie, and T. Ogata, ICDL-EpiRob 2015.

44

AI for Sciences

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DARPA Chicago Univ. Manchester Univ. Microsoft

Reading

Assembly

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controllingstates of thenetwork

A.Rzhetsky(U.Chicago)

49

https://www.youtube.com/watch?v=61pQZYmhGs
g50

The Need for Text Mining
Types of documents
Full papers
Abstracts
Reports, discharge summaries
EMR
Textbooks, monographs
Grey content, online
discussion forums

MEDLINE
2005: ~14M
2009: ~18M
2013: ~22M
2015: ~26M
Overwhelming information in textual,unstructured format

By S. Ananiadou
51(U. Manchester)

http://www.nactem.ac.uk/EventMine/
Event Extraction
Finding events ( trigger mentions , event types , and typed arguments
including locations ) involving genes or gene products

inhibits

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phosphorylation
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TRAF2

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domainFinding Evidence -EuropePubMed Central
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82,198,474 facts in 38,411,661 sentences
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Parsing pipeline run on 60 machines at EBI ~30
days
http://labs.europepmc.org/evf

By S. Ananiadou(U. Manchester) 53

Deep Reading: Reading with a Model
Goal: evaluate how TM systems process text inrelation to what is known about a pathway
Performers asked to produceRelationship/proposed change to the model(new/corroborating/conflicting information)
A model fragment describing the change
The source text supporting the change

By L.Hirschman(MITRE)Reading against a Model (1)monoubiquitination of Ras
enhances association with thedownstream effectors Raf andPI3-

CORROBORATING: We know thatRas binds Raf

By L.Hirschman(MITRE)

Reading against a Model (2)monoubiquitination of Ras
enhances association with thedownstream effectors Raf andPI3-Kinase

NEW MECHANISM: Ras binds PI3Kinase.
Ras

By L.Hirschman(MITRE)Reading against a Model (3), the RAS-ASPP
interaction enhances thetranscription function of p53

NEW RELATIONSHIP: RAS-ASPP complex
increases transcriptional activity of p53
Ras-> act(p(HGNC:P53), ma(tscript))

By L. Hirschman(MITRE)

Epistemic knowledge
Enriches event-based search systems
Discovery of new knowledge
Negation, uncertainty, speculative claims in literature

we hypothesized that the phosphorylation of TRAF2 inhibits

Miwa, Thompson, McNaught, Kell, Ananiadou (2012). Extracting semantically enriched
events from biomedical literature. BMC Bioinformatics 13, 10858

Extracting epistemic knowledge
SourceUncertainty

Negation

Analysis

By S. Ananiadou
59(U. Manchester)

Deep reading

existing components
custom components
existing components suppliedwith custom resources

By R. Batista(U. Manchester)Deep Reading

Fig.3 of Rak, et al., (2014) Database (Oxford) 2014, pii:bau070.

Deep ReadingÛ¦Ý¿¬ÜÞ

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Pocket Similarity Search using Multiple-Sketches (PoSSuM)
possum.cbrc.jp/PoSSuM/

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studio ousia : EC/Ad
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existing components
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By R. Batista(U. Manchester)MS closed the deal with Nokia. <-> Microsoft acquired the mobile
business of Nokia.
I see this wish coming true in the near future. <-> This desire seems
likely to be fulfilled in the near future.
Relying on team spirit and hard work, members defeated numerous
difficulties. <-> Members of the scientific team have overcome
various difficulties through their spirit of teamwork and hard work.
It isn't necessary to be rich to carry out good deeds for the publicwelfare. <-> It is not necessary that you should be rich to take partin public welfare works.
While imitating the West, we condemn prayers as old fashioned. <> As a result of blindly following the culture of Western nations, weregard virtuousness as outdated concepts.

5

Why N-best Parses?
[Source] Sometimes, blood pressure causes deficiencies in the heartprocess and heart disease[Target] At times blood pressure leads to disturbances in heart's
processes and heart diseases
Sometimes

At times
bloodpressureleadstoblood pressurecausesdeficiencies

in the heart process and heart disease

6 diseaseand heart[Source] Sometimes, blood pressure causes deficiencies in the heartprocess and heart disease[Target] At times blood pressure leads to disturbances in heart's
processes and heart diseases
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blood pressureleads
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deficiencies in the heart process
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7 diseaseand heart

Our Model

Gap

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Tuned hyper-parameters using Bayesian optimization (epochs) to optimize parsing quality
Have NOT separated a development set
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At training: beam 50, batch-size: 200, 10 iteration

Staged-Architecture

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9

ProtocolMaker
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Human Protocol VS. Robot Protocol

1.
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