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Relation Classification Between the Extracted Entities of Swedish Verdicts
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Relationsklassificering mellan extraherade entiteter ur svenska domar (Swedish)
Abstract [en]

This master thesis investigated how well a multiclass support vector machine approach is at classifying a fixed number of interpersonal relations between extracted entities of people from Swedish verdicts. With the help of manually tagged extracted pairs of people entities called relations, a multiclass support vector machine was used to train and test the performance of the classification. Different features and parameters were tested to optimize the method, and for the final experiment, a micro precision and recall of 91.75% were found. For macro precision and recall, the result was 73.29% and 69.29% respectively. This resulted in an macro F score of 71.23% and micro F score of 91.75%. The results showed that the method worked for a few of the relation classes, but more balanced data would have been needed to answer the research question to a full extent.

Abstract [sv]

Detta examensarbete utforskade hur bra en multiklass stödvektor- maskin är på att klassificera sociala relationer mellan extraherade personentiteter ur svenska domar. Med hjälp av manuellt taggade par av personentiteter kallade relationer, har en multiklass stödvektormaskin tränats och testats på att klassifiera dessa relationer. Olika attribut och parametrar har testats för att optimera metoden, och för det slutgiltiga exprimentet har ett resultat på 91.75% för båda mikro precision och återkallning beräknats. För makro precision och återkallning har ett resultat på 73.29% respektive 69.29% beräknats. Detta resulterade i ett makro F värde på 71.23% och ett mikro F värde på 91.75%. Resultaten visade att metoden fungerade för några av relationsklasserna men mer balanserat data skulle ha behövts för att forskningsfrågan skulle kunna besvara helt.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Support Vector Machines, Machine Learning, Relation Classification
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-206829OAI: oai:DiVA.org:kth-206829DiVA: diva2:1093977
External cooperation
Findwise AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-05-19 Created: 2017-05-08 Last updated: 2017-05-19Bibliographically approved

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