Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Describing golf shots using Natural LanguageGenerationAn: An investigation of a suitable method for implementing a Natural LanguageGeneration system with the aim of being a useful resource for golfers
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Natural Language Generation, NLG, is the translation ofraw data into legible and understandable text. It is currently being used in a variety of ways ranging from weatherforecasts to directions given by Google Maps. External NLG libraries are available for several programming languages, allowing more focus on the determining what would be included in the text generated.This report aims to investigate a suitable method of building an NLG system to be used with data provided by the Protracer software with the intent to provide golferswith text-based feedback of their game. Possible methods have been narrowed down to three approaches. The firstis simply outputting random feedback to the golfer with generic words and hope for the best. The second is usinga machine learning technique that takes shot parametersalong with a human interpretation of that shot. From this, the algorithm would acquire information about what buildsup a specific shot. Finally, the last method is using shotparameters along with human interpretation to construct an algorithm with observed threshold values to determine the shot type. Ultimately, the system constructed with the help of thereport was effective in classifying and describing golf ball trajectories. The difference between human interpretation and that of the system was negligible as the line between the classifications of a golf shot is very thin.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157515OAI: oai:DiVA.org:kth-157515DiVA: diva2:770327
Examiners
Available from: 2014-12-11 Created: 2014-12-10 Last updated: 2014-12-11Bibliographically approved

Open Access in DiVA

fulltext(591 kB)273 downloads
File information
File name FULLTEXT01.pdfFile size 591 kBChecksum SHA-512
511f135692ec35a2262077a7fae031ad995b3e8cb993d7f0dba2847d6b4fceb490faef13e18b25bcb2c53277d0e1049279ff501fd7d5f2980e22019874b4f751
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 273 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 404 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf