Mining for Lexons: Applying Unsupervised Learning Methods to create Ontology Bases

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Mining for LexonsApplying Unsupervised Learning
Methods to create Ontology Bases

• Marie-Laure Reinberger, Peter Spyns,Walter
  Daelemans, Robert Meersman
• CNTS - University of AntwerpSTARLab - Vrije
  Universiteit Brussel

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Project OntoBasis

• Elaboration and adaptation of Text Analysis
  tools for the building of specific domain
  Ontologies
• The role of Ontologies in Database and Web
  Semantics

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Outline

• DOGMA
• Text Mining
• Syntactic analysisClusteringEvaluationResults
• Conclusion

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A DOGMA inspired ontology

• Developing Ontology-Guided Mediation for Agents
  an ontology engineering approach
• Ontology Base consisting of binary relations or
  lexons extracted automatically
• Layer of Ontological Commitments (rules)

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First step Ontology Base

• Unsupervised extraction of relevant terms
• Grouping or clustering of those terms
• Medical domain
• 4M corpus - Medline abstracts

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Outline

• DOGMA
• Text Mining
• Syntactic analysisClusteringEvaluationResults
• Conclusion

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Syntactic analysis

• NP1Subject The/DT patients/NNS NP1Subject
  VP1 followed/VBD VP1 NP1Object a/DT '/''
  healthy/JJ '/'' diet/NN NP1Object and/CC
  NP2Subject 20/CD /NN NP2Subject VP2
  took/VBD VP2 NP1Object a/DT high/JJ level/NN
  NP3Object PNP P of/IN P NP physical/JJ
  exercise/NN NP PNP ./.

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Data selection

• Subject-Verb-Object structure selectional
  restriction functional relation
• Structures selection according to frequency
• Building of initial classes

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Clustering algorithms

• Soft clustering
• Hard clustering
• Clusters merging

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Soft clustering

• each verb associated to its class of co-occuring
  nouns
• nouns clustered according to the similarity
  between classes of nouns
• algorithm run as long as a cluster is modified

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Soft clusters

• - hepatitis infection disease cases syndrome-
  liver cirrhosis disease carcinoma HCC HBV virus
  method model- liver transplantation
  chemotherapy treatment- chemotherapy treatment
  vaccine injection drug- immunization vaccine
  vaccination

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Hard clustering

• each noun associated to its class of co-occuring
  verbs
• nouns clustered according to the similarity
  between classes of verbs
• each step measure lowered
• cut according to the percentage of nouns clustered

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Hard clusters

• - month year- children infant- concentration
  number incidence use prevalence level rate-
  course therapy transplantation treatment
  immunization

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Evaluation

• Use of WordNet
• Building of a set of WordNet pairs of nouns
• Considering the set of WordNet pairs ? recall
  WN pairs in clusters / WN pairs ? minimum
  precision (mP) WN pairs in clusters /
  pairs in clusters composed of WN words ?
  extrapolated precision (eP) ext. WN pairs in
  clusters / ext. pairs in clusters composed of
  WN words

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Comparison raw text vs parsed textsoft clustering
Parsed1 Clusters gt 15 elements
dismissed Parsed2 Clusters gt 15 and clusters lt 3
elements dismissed
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Comparison raw text vs parsed texthard clustering
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Raw text or parsed text?

• Hard comparable results
• Soft better for parsed text
• N-grams gain of processing time
• Parsed text possible labeling of the relation

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Soft clustering vs Hard
Soft1 Clusters gt15 elements dismissed Soft2
Clusters gt 15 and clusters lt 3 elements dismissed
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Soft or Hard?

• SoftDifferent semantic dimensions
  consideredProblem too many clusters, too many
  associations
• HardProblem only one semantic dimension of the
  word is considered

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Merging hard and soft clusteringon disease

• Hard clustering 1 cluster- disease transmission
• Soft clustering 6 clusters- antigen hepatitis
  virus protein disease- prevalence infection
  correlation disease
• Combination 2 clusters- hepatitis infection
  disease cases syndrome- liver cirrhosis disease
  carcinoma vaccine HCC HBV virus history method
  model

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on chemotherapy

• Hard clustering- (none) transplantation
  treatment
• Soft clustering- hepatitis blood factor HBV
  doses chemotherapy treatment vaccine vaccines
  vaccination injection drug immunization- liver
  chemotherapy treatment transplantation
• Combination- liver transplantation chemotherapy
  treatment

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Evaluation results (150-200 words)
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Turning to terms

• Catch terminology items we have missed so far
• Hard clustering on terms would allow a noun or
  part of a term to appear in different
  clustersface mask, mask, glove, protective
  eyewearimmunoadsorbent, immunoassay, immunospot

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Prospects

• Focus on terms and on the verb-object dependency
• Filter the terminology
• Use prepositional structures to establish links
  and build a network

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Example

• Cluster1 buy pay inc
• Cluster2 month year president
• Words found in WordNet buy pay month year
  president

WordNet pairs buy pay month year
Clustered pairs (WN words)buy pay month
yearyear president month president
Recall 2/2100Precision 2/450
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Turning to terms