http://2015.semantics.cc/julien-gon%C3%A7alves

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Named entity recognition and
disambiguation using an iterative
graph processing system
Julien Gonçalves

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Julien Gonçalves
VP research and partner at Reportlinker
Working on semantic technologies since 2004
@rlk_jgo
fr.linkedin.com/pub/julien-gonçalves/2/557/a21

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Who is Reportlinker?
ReportLinker is a technology company focused on providing
actionable information from global market research and data,
to Marketers, Analysts, Researchers and knowledge workers
in the enterprise.
4000 recurring clients around the world (HonneyWell, 3M, …)
A 10M€ Company
40 Employees with 50% engineers and semantic specialists

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What is ReportLinker?
Reportlinker finds, filters and organizes the latest industry data

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Find, Filter and Organize
30 million new documents analyzed / month
Document discovery NLP Search Engine
1 billion url verified / month
3 million documents available as relevant
Each part of the workflow is scalable

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Natural Language Processing
Text preprocessing
Converting
PDF/DOC/PTT format
to Text.
Lexical analysis
Parsing words and
sentences
Morphology analysis
Chapter/Table/Figure
detection using the
morphology of the original
document
Semantic analysis
Using a thesaurus
with 3.5 millions
ontologies
(industry,
geography, topic)
Storage
Structured,
annoted, sliced:
ready to be
searchable
Data relevance
Scoring each type of
data by relevance
(statistics, analysis, …)

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Semantic Analysis
Industry
Geography
Topic
A Data Lexical Platform using a thesaurus with 3 dimensions
350 industries, 3000 sub-industries
world regions, countries, main cities
Ontologies about industrial economics
(production, exportation, …)

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Semantic Analysis
Industry
Controlled vocabulary helps to find the right meaning of a term
Agribusiness
Food
Fruit and Vegetable
Fruit
apple banana
One term can be used for several meanings (ex:
“apple” as fruit or company).
The proximity with other concept in the thesaurus
helps to find the right meaning when found in the
same section of text.

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Semantic Analysis
How can we find, normalize and classify
the company names mentioned in our
reports ?

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Semantic Analysis : Company
Very simple approach: Use a database of company names as ontology
FAIL ! This approach did not work at all
We bought and used a database with 2 000 000 company names
Too many company names existing as common name (ex: “Post
Office”, “Table”, …)
To avoid the noise, we need to match more context in order to be
sure of the right meaning of a concept.

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Semantic Analysis : Company
Millions of companies exist around the world
Company context changes very often (acquisisions, new
activities, ...)
Hundreds of companies can have the same name
To be able to disambiguate, we need additional context
for each company concept.

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Our approach
STEP 1
Hypotheses
STEP 2
Inferences
STEP 3
Analysis &
Classification
To create our own database of company names with
additional context for disambiguation.
To exploit our content (110 millions documents) to discover
and identify company names, people, products.
To use an inference engine to build a relational graph with
verified concepts and contexts.
To use this new base of verified companies, enriched with
contexts, to find the right companies in our content.

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Step 1: Hypotheses
For each document analysed, we extract several “hypotheses”
(unverified facts) using text mining rules
Identification of a concept (the probability that its a company,
person, product, …)
We mainly have 3 types of hypotheses:
Relation between 2 concepts (context proximity between 2
concepts in the document)
Proximity between a concept and an industry/country
(context proximity with an other dimension in the document)

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Step 1: Hypotheses
In march 2010, Toto inc. acquired Thingso corp., the new CEO
Kevin Sherpa wants to be present in China to sell the new Xbrid3.
Example
“Kevin Sherpa” is guessed as a person name (NER rules).
“Toto inc.” and “Thingso corp.” are guessed as company (using
NER rules). More the pattern is “safe”, more the hypothese is
strong.
“China” is a country (Ontology).
“Xbrid3” is an unidentified named entity (NER rules).

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Step 1: Hypotheses
Toto inc.
Label Context Industry / Geography
Toto inc. Thingso corp. (C) / Kevin Sherpa (P) / Xbrid3 China
Thingso corp. Toto inc. (C) / Kevin Sherpa (P) / Xbrid3 China
Thingso
corp.
Kevin
Sherpa Xbrid3

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Step 1: Hypotheses
To validate these hypotheses we need to find more facts verifying
the same hypotheses.
Data volume is one of the key elements of this approach
We mine billions of sentences from economic reports and 3
million news update every month.
Each hypothese brings new information and new contexts
around a company concept.
More an hypothese is verified with several sources, more
chance it has to become a verified fact.

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Step 2: Inferences
An inference engine verifies all the hypotheses around each
concept in order to keep only the verified facts
C1
C2
P
2
P
1
From millions/billions of sub-graphs (each scope of context), we
obtain 1 final consolidated graph composed of only thousands of
sub-graphs.

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Step 2: Inferences
Apache Giraph is an iterative graph processing system built
for high scalability.
Giraph implements the Pregel model and other features that
makes it easy to use graph computation.
Giraph loads all the graph in-memory, computation is very
quick.
Giraph is highly scalable.

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Step 2: Inferences
Graph reduction continues until we can’t reduce the graph anymore
Toto Inc. #1
Kevin Sherpa
Thingso corp.
Xbrid3
Toto Inc. #2
Kevin Sherpa
David Rego
Xbrid3
Toto Inc. #3
Thingso corp.
David Rego
Xbrid Project
Neko Ltd.
Toto Inc. #4
Kevin Sherpa
Xbrid Project
Neko Ltd.
China China
US
China
Toto Inc. #1 Kevin Sherpa
David Rego
Thingso corp.
Xbrid3
China
Toto Inc. #2
Xbrid Project
Kevin Sherpa
Xbrid3
China
US
Toto Inc. #1
Xbrid Project
Kevin Sherpa
David Rego
Thingso corp.
Neko Ltd.
Iteration 1
Iteration 2
David Rego
Thingso corp.
Neko Ltd.
China
US

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Step 2: Inferences
The final graph is filtered to obtain a base of verified companies
Only the best context is kept for each company name
(context frequently related to the company)
Special iterations are processed to normalize company
names having very close names (ex: “Google France” and
“Google Fr”).

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Step 3: Semantic Analysis
Company
Name
Name to Match / Alias Contexts Industry / Geography
Toto inc. Toto inc.
Toto incorporated
Toto
Xbrid Project
Kevin Sherpa
Xbrid3
David Rego
Thingso corp.
Neko Ltd.
...
China
US
Apple inc. Apple inc.
Apple incorporated
Apple
Tim Cook
iPhone
iPad
Steve Jobs
...
US
World
More a company name is “common”, the more it will need a
better diversity of context to be verified (common noun, several
company with the same names, high frequency in the corpus)

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Step 3: Semantic Analysis
Kevin Sherpa said “Toto forecasts to double its revenue in China
selling the new Xbrid3.”
1) “Toto” is a possible name to match, normalized as “Toto inc.”
2) “Toto” is found in this text, we load all the contextual terms terlated to this
concept in order to disambiguate and select the right concept.
3) Contextual terms are found, “Toto” is classified as “Toto inc.” in this text.

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Step 3: Semantic Analysis
Contextual
terms related to
companies
Verified
company names
NLP
Content to analyse
Load in memory
Checking contextual
terms
company found:
disambiguated and
classified
Company names that are eligible are loaded in memory (NLP
process)
Contexts are loaded in memory in a remote cluster (Redis)

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Beta version: Statistics
400 million hypotheses
2 million documents analysed
graph nodes: 27 million
graph edges: 380 million
> 400 000 companies verified, enriched with contexts

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Conclusion
Using Big Data analytics we found a very good approach to
discover, disambiguate and normalise company names. This
solution works because we succeed in resolving 3 main issues:
Data volume
Pattern detection to discover hypotheses (NER rules)
Optimized algorithms for the inference engine

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QUESTIONS ?