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Automatic extraction? • Structural and lexical properties of definitions differ considerably
Automatic extraction? • ‣ Structural and lexical properties of definitions differ considerably Difficult to automatically extract semantic relations from definitions
Page 1 and 2:
DanNet From Dictionary to Wordnet J
Page 3 and 4:
• • • DanNet Lexical-semantic
Page 5 and 6:
1. Introduction Outline 2. Characte
Page 7 and 8: • • • • Den Danske Ordbog W
Page 9 and 10: Semantic description Systematic dom
Page 11 and 12: Semantic description Hyperonym
Page 13 and 14: Semantic description Collocational
Page 15 and 16: Semantic description
Page 17 and 18: 1. Introduction Outline 2. Characte
Page 19 and 20: Homonymy & polysemy celle ‚cell
Page 21 and 22: informatik ‚informatics‘ Establ
Page 23 and 24: Example: Hyponymy? kirsebærtræ
Page 26 and 27: Building the hierarchy TOP genstand
Page 28 and 29: Building the hierarchy TOP genstand
Page 30 and 31: • • Coding relations What is do
Page 32 and 33: Extraction of telic role fjernsyn
Page 34 and 35: Extraction of telic role fjernsyn
Page 36 and 37: Hypothesis
Page 38 and 39: Hypothesis Corpus query ‣ VPs in
Page 40 and 41: Results of corpus query query VP he
Page 42 and 43: Why this bad coverage?
Page 44 and 45: Why this bad coverage? 1. Definitio
Page 46 and 47: 1. GE that can VP-inf 2. GE that is
Page 48 and 49: A statistical approach
Page 50 and 51: • A statistical approach Frequenc
Page 52 and 53: • • A statistical approach Freq
Page 54 and 55: • • • • • • Some salien
Page 56 and 57: Automatic extraction?
Page 60 and 61: Automatic extraction? • ‣ ‣ S
Page 62 and 63: 1. Introduction Outline 2. Characte
Page 64 and 65: Cheap Expensive Conclusion Reusing
Page 66 and 67: Cheap Expensive Conclusion Reusing
Page 68 and 69: Conclusion The DanNet approach
Page 70 and 71: Cheap Expensive Conclusion The DanN
Page 72: Cheap Expensive Conclusion The DanN
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