T-REX: A Domain-Independent System for
Automated Cultural Information Extraction
Massimiliano Albanese
V.S. Subrahmanian
University of Maryland Institute for Advanced Computer Studies
College Park, Maryland, USA
Cognitive Architecture for Reasoning about Adversaries
Introduction
 Several applications require the ability to extract fine-grained
information from huge text collections
» Intelligence agencies may need detailed information about
diverse cultural groups around the world in order to understand
and model their behavior
» A real-time “violence-watch” around the world would require
the ability to identify several attributes for every “violent event”
reported in the online press
 Traditional search engines
» Are not able to provide such information without sorting through
a long list of documents
» Are not able to integrate information from different sources
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Key contributions
 Domain-independent framework for information extraction
» A schema describing the information the user wants to extract is
provided as an input
 Key features
» Scalability: the system is designed to massively scale to large volumes
of data
• It currently searches through 109 online news sites from 66 countries
around the world, processing about 45,000 articles/day (about 10 millions
distinct urls explored so far, with 7 millions triples extracted)
» Multilingual support: the system is designed to work with different
languages
• English, Spanish and Chinese
» Flexibility: several elements can be easily customized
• List of sources, topics of interest, type of information to extract
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T-REX architecture
Crawling and parsing
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Multilingual Annotation Interface
Sentence being annotated
Parse tree edit panel
List of triples that can be
extracted from the sentence
Constraint selection
panel
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Annotation Process: Motivation
 The same fact can be reported in many slightly different ways
» At least 73 civilians were killed February 1 in simultaneous suicide
bombings at a Hilla market
» More than 73 civilians were massacred in February in suicide attacks at
a Hilla marketplace
» 74 people were killed on February 1, 2007 in multiple bombings at a
Hilla market
 Other similar events may be reported through similar sentences, describing
the same set of attributes
» About 23 U.S. soldiers were killed in August 2005 in a suicide attack in
Baghdad
 Sentences describing the same type of fact in slightly different ways can be
grouped into a single class
» Learning an “extraction rule” for each class of interest to a given
application enables to extract the desired information from any article
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Annotation Process: Step 1
At least 73 civilians were killed
February 1 in simultaneous suicide
bombings at a Hilla market
The annotator is presented with
one or more parse trees for the
sample sentence
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Annotation Process: Step 2
The annotator marks as
“variable” all the nodes that
may have different text in other
sentences of the same class
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Annotation Process: Step 3
If needed, the annotator add
constraints to variable nodes
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Annotation Process: Constraints
 IS_ENTITY
» restricts a noun phrase to be a “named entity”
 IS_DATE
» restricts a noun phrase to be a temporal expression
 X_VERBS
» restricts a verb to be any member of a class X of verbs
• e.g. the constraint MURDER_VERBS requires a verb to be any of
the following: kill, assassinate, murder, execute, etc.
 X_NOUNS
» restricts a noun to be any member of a class X of nouns
• e.g. the constraint ATTACK_NOUNS requires a noun to be any of
the following: assault, attack, clash, etc.
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Annotation Process: Step 4
The annotator describes the
semantics of the annotated sentence
in term of triples, mapping attributes
to variable nodes
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Annotations in Multiple Languages
English
Spanish (Español)
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Chinese simplified (中文)
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Rule Extraction Engine
 An extraction rule is of type Head  Body
 A rule is learned through the following steps
» abstraction
• each variable node is assigned a numeric
identifier, its text and child nodes are
removed
› the model becomes independent of the
particular sentence
» body definition
• the body of the rule is built by serializing
the parse tree of the annotated sentence
in Treebank II Style
» head definition
• the head is defined as a conjunction of
RDF statements, one for each triple
defined in the last step of the annotation
process
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Rule Matching Engine (1/2)
 Extracts RDF triples, by matching sentence from texts being
analyzed against the set of extraction rules
Continuously fetches documents
relevant to the application of interest
If the parse tree of a sentence satisfies
the condition in the body of a rule an
RDF triple is instantiated for each
statement in the head of the rule
CompareNodes() determines if the
parse tree of a sentence satisfies the
condition in the body of a rule
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Rule Matching Engine (2/2)
 CompareNodes() recursively explores the parse tree of the
sentence being processed and the annotated parse tree of a rule
Checks satisfaction of constraints
for variable nodes
Checks constant nodes
Pairwise compares child nodes
of non terminal nodes
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Example of Matching
e.g. “About 23 U.S. soldiers were killed August 23 in a suicide attack in Baghdad”
The sentence satisfies the body of the rule
Var#1
Var#2
Var#3
Var#4
Var#5
Var#6
Var#7
=
=
=
=
=
=
=
“About 23”
“U.S. soldiers”
“were”
“killed”
“August 23”
“a suicide attack”
“Baghdad”
(KillingEvent9,victim,U.S. soldiers)
(KillingEvent9,numberOfVictims,about 23)
(KillingEvent9,date,August 23)
(KillingEvent9,location,Baghdad)
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Example of extracted data (1/2)
At least 22 Hindus were killed by suspected Muslim militants in India's Jammu and Kashmir state
Monday, the police said
Event data
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Example of extracted data (2/2)
Link depth 2 from
Pushtuns
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T-REX implementation
 The implementation of T-REX consists of several components
running on different nodes of a distributed system
» Multilingual Annotation Interface: web-based tool, that is part
of the web interface of T-REX (implemented as a Java Applet)
» Annotated RDF Database System for storage of annotated
RDF triples: the underlying relational DBMS is PostgreSQL 8.2
» Rule Matching Engine: a pipeline of several components
• Crawler: explores news sources for relevant documents
• Parsers for every language: process sentences from relevant
documents, producing constituent trees in Treebank II Style
• Extractor: implements the Rule Matching Engine logic
 Distribution, Database Partitioning, and Multithreading ensure
scalability
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Conclusions
 We have presented a general, multi-lingual and flexible
framework for information extraction
» Domain specific application are enabled by targeting the
»
extraction to the instantiation of a schema of interest
Addition of other languages is a relatively simple task, once a set
of linguistic resources are available for those languages
 We have implemented a complex prototype that has proved to
» effectively extract information for different applications
» scale massively
 Future efforts will be devote to
» define pruning strategies to make the extraction process faster
» define strategies to manage inconsistencies in the extracted data
» extend the system to other languages (mainly Asian languages)
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