Scientific Paper Analysis
Yuji Matsumoto
Computational Linguistics Lab
Graduate School of Information Science
March 6, 2015
Big Data Symposium
at NAIST

Large Scale Text Data
Data on the Web
 SNS: twitter, blog
 Wikipedia
 News, …
Scientific/Technical documents
 Scientific Papers
 Legal documents: law reports, casebooks
 Patent documents

Knowledge Bases
Constructed manually
 WordNet, Domain ontologies
Constructed by community
 (Wikipedia)
 Freebase
Constructed automatically
 NELL: Never-Ending Language Learning
 MindNet

Applications
Knowledge Graph (Google)
 Knowledge extracted from
Freebase, Wikipedia, …
Watson (IBM)
 Extracted from Wikipedia
 Deep QA

Structures of KB
Linked structure
 entities and relations
 PDF
 Entity: person, country, products, etc
 Relation: born_in(Barack Obama, Honolulu)
locates_in(Honolulu, Hawaii)
state_of(Hawaii, USA)

Natural Language Analysis
How text is analyzed
 Word segmentation, Part-of-speech
tagging
 Named entity recognition
 Syntactic parsing
 Semantic disambiguation
 Semantic parsing
 Discourse analysis

Linked Knowledge Extraction
Named entity recognition
 Extraction of entities, concepts
Syntactic dependency parsing
 direct dependency between entities
Semantic parsing
 predicate argument structure analysis
 subject-predicate-object, relation between entities
Discourse analysis
 co-reference – the same entity by different
mentions
 relation between facts: temporal, causal

8
We analyzed the effect on the binding and
the activity of transcription factors at a regulatory element.
TPA induction inhibits the binding of the transcription factor NF-E2 to
this transcriptional
control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause Theme
Theme
Theme Theme
S1
S2
S3
Semantic Parsing: Example
Katsumasa Yoshikawa, Sebastian Riedel, Tsutomu Hirao, Masayuki Asahara, Yuji Matsumoto,
"Coreference Based Event-Argument Relation Extraction on Biomedical Text,“
Journal of Biomedical Semantics, Volume 2, Supplement 5, S6, October 2011

9
"this element" in S2 is coreferent to…
"a regulatory element" in S1
We analyzed the effect on the binding and
the activity of transcription factors at a regulatory element.
Corefer
TPA induction inhibits the binding of the transcription factor NF-E2 to
this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause Theme
Theme
Theme Theme
S1
S2
S3
Co-reference analysis

10
The true argument (Theme) of binding is "a regulatory
element“ and "this element" is just an anaphor of it
Transitivity enables us to conflate the information
We analyzed the effect on the binding and
the activity of transcription factors at a regulatory element.
(B) Corefer
(C) Theme
TPA induction inhibits the binding of the transcription factor NF-E2 to
this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause Theme
Theme
Theme (A) Theme
S1
S2
S3
(A) Theme & (B) Corefer => (C) Theme
Information conflation

11
We analyzed the effect on the binding and
the activity of transcription factors at a regulatory element.
Corefer
Theme
TPA induction inhibits the binding of the transcription factor NF-E2 to
this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause Theme
Theme
Theme Theme
Theme
CoreferTheme
S1
S2
S3
Discourse analysis

Syntactic parsing
NE chunking
Part-of-Speech(POS)
tagging
Syntactic parsing
NE chunking
Part-of-Speech(POS)
tagging
Predicate-argument
Structure analysis
Coreference
resolution
Relation
extractionsemantic/
context
processing
Machine Learning /
Knowledge Acquisition
Document Structure AnalysisDocument Structure Analysis
Knowledge
Bases
(Dmain
Ontologies)
Knowledge
Bases
(Dmain
Ontologies)
NLP Technologies for Document Analysis
12

What we can do with Scientific
Papers
Knowledge extraction (domain knowledge)
New fact discovery
Content-aware paper search
Summarization
 Automatic generation of abstracts
 Keyword generation
 Survey generation
Recommendation of related papers
Similar article/case search
 Structural similarity: papers, law reports, patents

Example: Structured Abstract Generation
14

Related Project
Big Mechanism (2014.07-, by DARPA)
 http://www.darpa.mil/Our_Work/I2O/Programs/Big_Mechanism.aspx
 The Big Mechanism program aims to develop
technology to read research abstracts and papers
to extract pieces of causal mechanisms, assemble
these pieces into more complete causal models,
and reason over these models to produce
explanations. The domain of the program is
cancer biology with an emphasis on signaling
pathways.

Architecture of Big Mechanism
from Paul Cohen, “DARPA’s Big Mechanism Program”

Deep Language Analysis
Complex sentence structure analysis
Robust Semantic Parsing
Discourse Analysis
 Co-reference
 Causal / Temporal relation
Representation and Reasoning
Explanation / Anticipation
Confidence/credibility (of extracted facts /
what is written in documents)

Large-scale Text Data
syntactic dependency structure
argument structure, coreference
rhetorical / document structure
POS tags, phrase/NE chunking
relations （ temporal, causal,
entailment ）
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Knowledg
Ontology
Language Processing and Document
Analysis Layers
Document Analysis
(Document Understanding, Similarity-based Search,
Knowledge Discovery/Assembling)

We may be able to do more
Research Trend Survey
Research (paper) Evaluation
 Content-aware citation analysis
Innovation Foresight
 Eg: Foresight and Understanding from Scientific
Exposition (FUSE) Project
 http://www.iarpa.gov/index.php/research-programs/fuse
Collaboration with people in application areas
who need to read/understand documents