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Relation types : hyperonymy, meronymy, definition, ...
Select 8 corpora in 8 different domains of 3 genres
Technical writings , Scientific papers, Handbooks
Evaluation measures
Recall : only available for definition patterns
Precision : correct phrases / matched phrases
No measure of the relevance for the ontology
Results
Dependency of a pattern efficiency and meaning on the textual genre
Requires manual evaluation
Precise but not very productive (silence)
Adapted to well written, pedagogical texts
Requires to capitalize patterns and experiments with pattern
performances
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
Cameleon
Evalution
Going further evaluation
Conclusions from the evaluation
Claims about pattern reuse
There is no generic (universal) pattern
But any pattern should be reusable after adaptation
Claims about pattern manual definition
Manual pattern definition is time consuming
Pattern definition requires linguistic competency
Reuse of already defined patterns reduces this cost
Patterns adaptation to each corpus improves efficiency
Claims about pattern evaluation
Capitalize previous uses of patterns together with the corpus and evaluation
scores
Pattern efficiency may vary a lot according to the corpus
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
Cameleon
Evalution
Going further evaluation
Our goals from the first evaluation
Answers to evaluation
Claims about pattern reuse
validation of "generic" patterns through uses
construction of annotated pattern base depending on domain
Claims about pattern manual definition
learning to improve time pattern acquisition
NLP assistance to user at each step (building and validating)
capitalization of patterns
Toward more assistance
=> how to improve the existant tool with learning and NLP technics ?
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
General Overview
Processing Steps
Interactive Interfaces
1 State of the art
2 Pattern Approach Contribution
3 Framework
General Overview
Processing Steps
Interactive Interfaces
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
General Overview
Processing Steps
Interactive Interfaces
Processing Steps
User validation
Corpus
Ontology
Pattern
Base
Enriched Ontology
Ontology Integration
∂REL(Ci, Cj) Watson
INPUT
OUTPUT
tagged
text
Pattern
Base
Enriched Pattern Base
NLP chain
Pattern Matching
Rel(ti, tj)
Relation Proposal
REL
Pattern Proposal
PRELi
Ontology enrichment Pattern Base enrichment
Identified Relation Unknow Relation
Concept Identification
RELC(Ci, Cj)
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
General Overview
Processing Steps
Interactive Interfaces
Input
User validation
Corpus
Ontology
Pattern
Base
Enriched Ontology
Ontology Integration
∂REL(Ci, Cj) Watson
INPUT
OUTPUT
tagged
text
Pattern
Base
Enriched Pattern Base
NLP chain
Pattern Matching
Rel(ti, tj)
Relation Proposal
REL
Pattern Proposal
PRELi
Ontology enrichment Pattern Base enrichment
Identified Relation Unknow Relation
Concept Identification
RELC(Ci, Cj)
Required Ressources
Lightweight Ontology
Pattern Base
Corpus
Marie Chagnoux, Nathalie Hernandez, Nathalie Aussenac Pattern-based Approach
State of the art
Pattern Approach Contribution
Framework
General Overview
Processing Steps
Interactive Interfaces
Inputs
A lightweight Ontology defined with
a structure : tuple S := {C,R,, @R}
a lexicon : a tuple L : {LC, LR, F,G}
For our approach : at least a hierarchy of concepts defined with
a lexicon L0 := {LC, F} a structure S0 := {C, }.
A corpus
extracted by experts from existing document collections
tagged with a morpho-syntactic analysis (tozenisation, lemmatization
and POS)
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