1 Simple Algorithms for Compl ex Relation Extraction with Applications to Biomedical IE Ryan McDonald Fernando Pereira Seth Kulick CIS and IRCS, University of Pennsylvania, Philadelphia, PA Scott Winters Yang Jin Pet e White Division of Oncology, Children’s Hospital of Pennsylva nia, Philadelphia, PA ACL 2005
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1 Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE Ryan McDonald Fernando Pereira Seth Kulick CIS and IRCS, University.
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Simple Algorithms for Complex Relation Extraction with Applications to Bio
medical IE
Ryan McDonald Fernando Pereira Seth KulickCIS and IRCS, University of Pennsylvania, Philadelphia, PA
Scott Winters Yang Jin Pete WhiteDivision of Oncology, Children’s Hospital of Pennsylvania, Philadelphia, PA
ACL 2005
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Abstract
• Simple two-stage method for extracting complex relations between named entities in text. – n-ary relation– first stage: create a graph from pairs of entities– two stage: maximal cliques in the graph
• Experiment on biomedical text
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Introduction - 1/2
• n-ary relation– The relation is definded by the schema (t1,…, tn)
• ti is entity types
– The tuple in the relations is a list of entities (e1,...,en) • Type(e1)=t1 or ei=
• Example : – Type : {person, job, company}
• “John Smith is the CEO at Inc. Corp. “• (John Smith, CEO, Inc. Corp.)• “Everyday John Smith goes to his office at Inc. Corp.”• (John Smith, , Inc. Corp.)
• 447 abstracts selected from MEDLINE– 4691 sentences– 4773 entities and 1218 relations– Of the 1218 relations :
• 760 have two , 283 have one , 175 have no arguments• 38% cannot be handled using binary relations• 4% of the relations annotated are non-sentential • Maximum recall : 96%
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Experiments-2/2
• MC: – Uses the maximum entropy binary classifier coupl
ed with the maximal clique complex relation reconstructor.
• PC: – Same as above, except it uses the probabilistic cliq
ue complex relation reconstructor.
• NE: – A maximum entropy classifier that naively enumer
ates all possible relation instances as described in Page 7.
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Experiments : Results-1/2
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Experiments : Results-2/2
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Conclusions and Future Work
• Complex relation extraction:– Binary relation learning: Maximum Entropy
Classifier – Finding maximal cliques in graph– Genomic variation relations
• Future work– Parse trees– Learn how to cluster vertices into relational groups– A vertex/entity can participate in one or more
relation
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• Learning Field Compatibilities to Extract Database Records from Unstructured Text – M Wick, A Culotta, A McCallum - (EMNLP 2006)
• Using Dependency Parsing and Probabilistic Inference to Extract Rela-tionships between Genes – B Goertzel, H Pinto, A Heljakka, IF Goertzel, M –(B
ioNLP 2006)
• Relation Extraction for Semantic Intranet Annotations – L Specia, C Baldassarre, E Motta - kmi.open.ac.uk – Relation Extraction for Semantic Intranet Annotatio