Automatic detection of new domain-specific words, using document ...

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4.2. Problems • Highly frequent domain-specific types in a text only count 1 −→ Largest intersection of D and the set of text tokens, W • Domains with large vocabularies are likely to get a better score −→
4.2. Problems • Highly frequent domain-specific types in a text only count 1 −→ Largest intersection of D and the set of text tokens, W • Domains with large vocabularies are likely to get a better score −→ Size of a domain-specific vocabulary, |D|
Page 1 and 2: Automatic detection of new domain-s
Page 3 and 4: 4.3 To compute a score for a certai
Page 5 and 6: 1. Background: updating the DDO •
Page 7 and 8: 1. Background: updating the DDO •
Page 9 and 10: 1.1. Source of new vocabulary • M
Page 15 and 16: 1.3. Prerequisites for the updating
Page 17 and 18: 1.3. Prerequisites for the updating
Page 19 and 20: 2. Data: the DDO Corpus The Corpus
Page 27 and 28: 3. Analysis: deriving domain vocabu
Page 29 and 30: 3.1. Examples: most salient types f
Page 31 and 32: 3.2. Quantitative problems with the
Page 33 and 34: 3.3. Qualitative problems with the
Page 35 and 36: 4. Text classification 4.1. Startin
Page 37: 4.2. Problems • Highly frequent d
Page 41 and 42: 4.2. Problems • Highly frequent d
Page 43 and 44: 4.3. To compute a score for a certa
Page 55 and 56: 5. Comparison: determining new word
Page 57 and 58: 5.3. Classification • The text is
Page 63 and 64: 5.6. New sense candidates Type fDDO
Page 65 and 66: 6. Discussion, future, and conclusi
Page 71 and 72: 6.1. Basic decisions - and question
Page 73 and 74: 6.1. Basic decisions - and question
Page 75 and 76: • Classification procedure - Refl
Page 77 and 78: • Classification procedure - Refl
Page 79 and 80: • Vocabulary extraction - Based o
Page 81 and 82: 6.2. Testing in progress • Mutual
Page 87 and 88: 6.3. Future work • Domain-specifi
Page 89 and 90:
6.3. Future work • Domain-specifi
Page 91 and 92:
6.4. Conclusion Good: • The mecha
Page 93 and 94:
6.4. Conclusion Good: • The mecha
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