A Survey on Information Extraction from Documents Using Structures of Sentences

A SURVEY ONINFORMATION EXTRACTIONFROM DOCUMENTSUSING STRUCTURES OF SENTENCES

Chikayama Taura Lab. M1 Mitsuharu Kurita

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INTRODUCTION Current search systems are based on 2

assumptions

1. Users send words, not sentences2. The aim is finding documents which is

related to the query words

We are unconsciously get to select words which will appear nearby the target information

In some cases this clue doesn’t work well2

INTRODUCTION For more convenient access to the

information Analysis of the detail of question

To know the target information

Analysis of the information in retrieved documents To find the requested informationInformation Extraction

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OUTLINE Introduction Overview of Information Extraction (IE) IE with pattern matching IE with sentence structures

Frequent substructure Shortest path between 2 words Applying the kernel method for structured data

Conclusion

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INFORMATION EXTRACTION What is Information Extraction?

A kind of task in natural language processing Addresses extraction of information from texts

Not to retrieve the documents Originated with an international conference

named MUC

Message Understanding Conference (MUC) Competition of IE among research groups Set information extraction tasks every year

between 1987-19975

MUC COMPETITION An example of MUC task

MUC-3 terrorism domainInput: news articles

(some of them include terrorism event)

Output: the instances involved in each incident

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MUC COMPETITION Pattern matching or linguistic analysis

At that time (1987-1997), there were many difficulties to use advanced natural language processing

Therefore, most of competitors adopted pattern matching to find instances

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Conclusion

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EXAMPLE OF PATTERN MATCHING CIRCUS [92 Lehnert et al.]

Each pattern consists of “trigger word” and “linguistic pattern”

Pattern: kidnap-passiveTrigger:

“kidnap”Linguistic pattern:

“<subject> passive-verb”Variable:

“target”

“The mayor was kidnapped

by terrorists.”1. “kidnap” activates the

pattern2. “was kidnapped” is a

passive verb phrase3. The subject “mayor” is

the target

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PROBLEMS OF PATTERN MATCHING It takes a huge amount of time to create

patterns In many cases, they were handwritten

It depends a lot on the target domain It is difficult to adapt to the new task

Automatic constructionof patterns

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THE EARLIESTAUTOMATIC PATTERN

GENERATION AutoSlog [93 Riloff et al.]

Creates the patterns for CIRCUS automatically Training data: articles tagged the target word

Created 1237 patterns from 1500 tagged texts Only 450 of them were judged to be valid by

human

“The mayor was kidnapped

by terrorists.”

Pattern: kidnap-passiveTrigger:

“kidnap”Linguistic pattern:

“<subject> passive-verb”Variable:

“target”

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Recently it has become possible to use deeper linguistic analysis

Some studies are addressing new IE tasks using these linguistic resources and machine learning approach

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Conclusion

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SENTENCE STRUCTURES Dependency Structure

Describes modification relations between words One sentence makes up a tree structure

Predicate-Argument structure Describes the semantic relations between

predicate and argument One sentence makes up a graph structure

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DIFFICULTIES TO USE STRUCTURED DATA Most of the machine learning algorithms deal

with the data as feature vectors

It is difficult to express structured data (e.g. trees, graphs) as vectors

The ways to use sentence structures for IE Frequent substructures Shortest paths between 2 words Applying the kernel method for structured data

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Conclusion

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IE WITHSUBGRAPH OF SENTENCE STRUCTURES

On-Demand Information Extraction[06 Sekine et

al.] Create extraction patterns on-demand and

extract information with itquery Relevan

tarticles

FrequentSubtreeMining

Article database Dependency analyzer

Table of Information

Dependency trees

Subtree patterns

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EXPERIMENTAL RESULTS Generated patterns

Found patterns for a query“merger and acquisition” (M&A)

Extracted Information For the query “acquire, acquisition, merger, buy,

purchase”

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<COM1>

<agree to buy>

<COM2>

<for MNY>

<COM1>

<will acquire>

<COM2>

<for MNY>

<a MNY merger>

<of COM1>

<and COM2>

EXPERIMENTAL RESULTS Very quick construction of patterns

In MUC, it is allowed to take one month ODIE takes only a few minutes to return the

result

No training corpus is needed ODIE learns extraction patterns from the data

Information about reprising event can be extracted well Merger and acquisition Nobel prize winners 19



Conclusion

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IE WITHSHORTEST PATH BETWEEN

WORDS Extraction of interacting protein pair

[06 Yakushiji et al.] Extract the interacting protein pairs from

biomedical articles Focus on the shortest path between 2 protein

names on predicate-argument structure Discriminate with Support Vector Machine (SVM)

Entity1 is interacted with a hydrophilic loop region

of Entity2.be

entity1interact

withregion

ofa

hydrophilicloop

entity2 21

PATTERN GENERATION Variation of Patterns

The extracted patterns are not enough Divide the patterns and combine them into new

patterns

Main PrepEntity Entity

………

X interact Ywithprotein regio

n of

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PATTERN GENERATION Validation of patterns

Some of these patterns are inappropriate Each patterns are scored by its adequacy to the

learning data

Feature vector

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TP: True PositiveFP: False Positive

SUPPORT VECTOR MACHINE (SVM) 2 class linear classifier Divide the data space with hyperplane Margin maximization Margin

maximization

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EXPERIMENTAL RESULTS Learning

AImed corpus 225 abstracts of biomedical papers Annotated with protein names and interactions

Extraction MEDLINE

14 million titles and 8 million abstracts Extracted data

7775 protein pairs 64.0% precision 83.8% recall

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Conclusion

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IE WITH THE KERNEL METHOD ON SENTENCE STRUCTURES

Kernel Method e.g. SVM

Data are used only in the form of dot products If you can calculate the dot product directly, you

do not have to calculate the vector Furthermore, you can use other functions as long

as they meet some conditions27

Raw data

vector space

classifier

Kernel function

RELATION EXTRACTION Relation Extraction with Tree Kernel

[04 Culotta et al.] Classify the relation between 2 entities

5 entity types(person, organization, geo-political-entity,

location, facility) 5 major types of relations

(at, near, part, role, social) Classify the smallest subtree of dependency tree

which includes the entities

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TREE KERNEL Represents the similarity between 2 tree-

shaped data Calculated as the sum of similarity of nodes

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Dequeue a node pair

Add the similarity

Find all child node sequence pairswhose main features of the nodes

are common

Enqueue the child node pairs

Is the queueempty?

Return the similarity

Enqueue root node pair

Start

End

Yes

No

CALCULATION OF TREE KERNEL Features of nodes

The similarity between nodes are defined as the number of common features (except the main features)

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Main features

CALCULATION OF TREE KERNEL

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A

B C D

E

A’

B’ D’

E’

F’

A

B

A

D

D

E

C’

A’

B’ C’

A’A

A’

B’

A’

D’

A

B C

D’

E’

X and X’ denote the nodes whose main

features are common

A

C

A’

C’

EXPERIMENTAL RESULTS Data set: ACE corpus

800 annotated documents(gathered from newspapers and

broadcasts) 5 entity types

(person, organization, geo-political-entity, location, facility)

5 major types of relations(at, near, part, role, social)

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Kernel Precision (%)

Recall (%)

Bag-of-words kernel 47.0 10.0Tree kernel 69.6 25.3



Conclusion

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CONCLUSION Overview of Information Extraction

The aim of information extraction Recent movement to use deep linguistic resource

The way to use sentence structures for IE Difficulties of using structured data in machine

learning Three different approaches to exploit them

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