Impact Objectives• Develop innovative technologies for the benefit of all people,
specifically related to Natural Language Processing (NLP)• Build a structured Knowledge Base that combines Wikipedia andan extended named entity through a Research By CollaborativeContribution scheme

Research By
Collaborative Contribution
Dr Satoshi Sekine is the leader of the SHINRA project that seeks to advance the use of knowledge within
Natural Language Processing (NLP) tasks. He discusses his research in more detail, the importance of theresource collaborative contribution scheme and the next steps for his studies
Can you begin bytelling us a little
about the research
you are currentlyengaged with?
I am the team leader
within the Language Information AccessTechnology Team at the RIKEN Center for
Advanced Intelligence Project in Japan. The
project has many aims, but much of ourwork is centred on developing innovativetechnologies for the benefit of all humans,
specifically those related to Natural Language
Processing (NLP), which is a branch of
Artificial Intelligence (AI).
What does NLP mean?
NLP is a way that machines can understandhuman language - which has many benefits,
including enabling humans and machines
to talk to each other and make sense of one
another. One notable aspect of our work isconcerned with creating a knowledge basethat can function as an effective resource forNLP.

Can you explain in layperson terms what
NLP involves and why it is such an important
subject to know more about?
NLP can be thought of as a subfield thatcombines linguistics, computer scienceand AI. By giving machines the ability tounderstand text and spoken words in asimilar way to humans, we can advance thefield of computing to an unprecedented
extent. Think about the ambiguities that exist
between humans when they communicate- but facial expressions and tone can help
with our understanding - we do not merely
rely on what is said or written. If a computercan understand the unbelievable amount of
information that exists on the Internet, thenit will lead to efficiencies that are simply notpossible if we just rely on humans.
Your main research topic is SHINRA. Can you
talk a little more about what this involves?
SHINRA is a collaborative contribution
scheme that utilises Automatic Knowledge
Base Construction (AKBC) systems. This
project aims to create a huge and wellstructured Knowledge Base essentialfor many NLP applications, such as QA,

dialogue systems and explainable NLP
systems. We are aiming for a top-down
design and a bottom-up population for thisproject, meaning we will use a name ontologythat has been well designed and thenpopulated this by crowdsourcing or using AI.
SHINRA is centred on building a structured
Knowledge Base that combines Wikipedia
and an extended named entity through a
Research By Collaborative Contribution
scheme. Part of this is the SHINRA2020ML task, which is to categorise 30-language
Wikipedia pages into fine-grained namedentity categories, called ‘Extended Named
Entity (ENE)’.
What are you hoping to focus on in thecoming years?
SHINRA is our most pressing concern, but
in the future we need to extend its scope
to include knowledge of more generalthings. Then there is knowledge such as
when it rains people use an umbrella. Allof this knowledge needs to be machinereadable - we need to think about what thismeans for machines because we have tothink differently in terms of how machines
understand as opposed to humans.
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Enabling machines tomake sense of Wikipedia
A team of researchers are working on the RIKEN Center for Advanced Intelligence SHINRA project
which aims to achieve scientific breakthroughs and make meaningful contributions to the welfare ofsociety and humanity using the development of innovative technologies

Everyone knows about Wikipedia. Many ofus have likely been chastised for using it in
our essays and articles, not least becauseof its reputation as being rather unreliable.
However, the fact is that Wikipedia is abeautiful idea - a community where people
write pages of information about anything
and everything, readily verify the informationthat is contained within those pages, ensurethat claims can be referenced (and if theycannot, explain that a citation is needed),and basically provide an exhaustive resourcethat could never fully be pored over by any
human, even if they were to live a thousand
lifetimes.
This latter point is in stark contrast tocomputers, which can trawl throughinformation at a rate that is many times
faster than any human. Put simply, there
is too much information ‘out there’ forany one individual to make sense of, but if
we can somehow find a means of utilisingmachines, then the potential benefits aregreat. Unfortunately, the vast majority of
information hosted on Wikipedia is written
in such a way that makes it clear it is forpeople to read, rather than for a machine toutilise and manipulate.
Now a team of researchers have cometogether to create a project called SHINRA
which is centred around a Resource byCollaborative Contribution (RbCC) scheme.
The project aims to ensure that theinformation on Wikipedia can be structured
in such a way that makes it possible forcomputers to use. The findings could havesignificant positive impacts on humanity,
not least because of the development ofan explainable AI system that uses deeplearning and structured knowledge.
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WORKING TOGETHERTO CONSTRUCT A RESOURCEDr Satoshi Sekine is the team leader of
SHINRA, the project which focuses on
AI, deep learning and Natural Language
Processing (NLP). He is based within theLanguage Information Access Technology
Team at the RIKEN Center for AdvancedIntelligent in Japan. ‘The ultimate aim ofSHINRA is to build a structured Knowledge
Base combining Wikipedia and an Extended
Named Entity (ENE),’ highlights Sekine.
‘We hope to achieve our aim using a RbCC
scheme, which is a novel endeavour forworking together to construct a resource toadvance the field of NLP and AI,’ he says.
ENE is a hierarchy that was designed anddeveloped to meet the increasing need fora wider range of named entity types. Thishierarchy is divided into three main classes:
name, time and numerical expressions.
Through the team’s observations, they
have determined that any question on anyspecific matter generally fits within one of

Dr Satoshi Sekine; speaking at SHINRA project meetingthese three categories and by using such ahierarchy, much of what we consider to becommon knowledge in newspaper articles,
encyclopedia entries and Wikipedia pages
can potentially be understood by machines
engaged in deep learning.
The team worked tirelessly to define theseclasses based on a criterion of frequently
occurring words and noun phrases,
which were then categorised into a classaccording to the meaning and usage ofeach word and/or phrase. ‘We extracted thecandidate named entity expressions fromnewspaper, Wikipedias and many othertypes of documents,’ outlines Sekine. ‘More
specifically, entities and proper nouns andnumerical expressions were extracted fromliterally thousands of different contexts,
before we assigned appropriate concept
class labels,’ he says. From there, the teamworked to use references to existing online
thesaurus entries and ontology sites to findinformation that matched the hierarchy.

We hope to achieve our aim using a RbCC scheme, which is a novel endeavour for working
together to construct a resource to advance the field of NLP and AI

BUILDING A STRUCTURED WIKIPEDIAThe ultimate goal of the project is building
a structured Wikipedia. However, to achievethis, the team believes there are three tasks
that need to be performed: categorisation,attribute extraction and attribute value
linking. The first task is centred on
categorising Wikipedia entities into ENE
categories, which was originally done inJapanese using a machine learning method,with human experts checking the results.
This was concluded in 2017, but a coupleof years later the team updated it to reflect
the 2019 version of Wikipedia. Ultimately,
the scope has been expanded to reflect 30
languages, with the team working throughthe RbCC scheme on machine learningtasks.
For attribute extraction, the idea is to
extract values of pre-defined attributes
for each category from each Wikipedia
entry. In 2018, the team achieved this forfive categories, then 30 categories in 2019
and an additional 47 categories last year.
Finally, for attribute value linking, the teamis working on mapping the attribute values
extracted from Wikipedia articles to thecorresponding entities. This will be done forseven categories throughout 2021.
A VALUABLE KNOWLEDGE BASE
The SHINRA project commenced in 2017and is still ongoing. One of the main ideas
is to create a Knowledge Base scheme
using the RbCC approach. Sekine and therest of his team are acutely aware of how
popular and ever-present AI will become in
the near future - and there are already many
examples of it permeating our daily lives,
whether that be at work or in our leisuretime. ‘One of the most crucial aspects of
the research the team is undertaking is toensure that the technology employed ineveryday scenarios is capable of explaining
its processes and/or decision making andthat this is based on the RbCC approach,’
explains Sekine. ‘If this can be achieved
then we can truly begin to utilise all of theinformation that is out there,’ he says.
In many ways Sekine and the team have
only just begun what they are ultimately
hoping to achieve. The key idea is that theycreate a Knowledge Base that can be usedby them in future studies (or even different

researchers in different studies) to advancethe ability of computers to not only makesense of what it is they are reading, butexplain the rationale of their thought. It may
sound scary, but as the capacity of machinelearning improves, the simple truth is thathumans can learn from another sentient‘thing’ for the first time in history. lProject Insights
PROJECT MEMBERS
Satoshi Sekine (Chair), Kouta Nakayama,
Akio Kobayashi, Masako Nomoto, MayaAndo, Michiko Goto, Asuka Sumida, Koji
Matsuda, Yu Usami, Masatoshi Suzuki,Yukino Baba and Akiva Miura
We appreciate all contributions from theparticipants of SHINRA project
CONTACTDr Satoshi Sekine
E: satoshi.sekine@riken.jp
W: http://shinra-project.info/
shinra2020ml/?lang=en

Members of the Language Information Access Technology Team (including intern students from aroundthe world)
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