The Corpus Thread - Det Danske Sprog- og Litteraturselskab

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6.3. Case study 136 To/AC kendte/AN russiske/AN historikere/NC Andronik/NP Mirganjan/NP og/CC Igor/NP Klamkin/NP tror/VA ikke/RG ,/XP at/CS Rusland/NP kan/VA udvikles/VA uden/SP en/PI "/XP jernnæve/NC "/XP ./XP De/PP hævder/VA ,/XP at/CS Ruslands/NP vej/NC til/SP demokrati/NC går/VA gennem/SP diktatur/NC ./XP [...] 27 As can be seen, the original PAROLE tags have been cut off after two characters, the first character giving the POS, the second one giving a POS subclassification. Inflectional information is not present in the tags used here – a simplification that should be avoided in a real setting. 28 Building an HMM from PAROLE is quite straightforward and it actually does POS tagging afterwards, however some problems need to be addressed: 1. The PAROLE Corpus is quite small in size, approximately 250,000 tokens 2. The tag set has not been adjusted to the actual needs and is probably too large (25 different tags) to allow building an HMM from a corpus of this size 3. Only sentences with known word forms, i.e. such contained in PA- ROLE, can be tagged on the basis of this HMM As corpus size cannot be augmented within the scope of the ongoing project, to cope with these restrictions, the tag set should be optimized as should the elements of the observable layer of the HMM. At the moment these are word forms but it might be worth trying to map them to more abstract representations by using a lexicon. The following experiment will address this approach. 6.3.2 Building a lexicon-based HMM The idea behind this approach is to map word forms in the text to their possible POS tags prior to training and analyzing by applying a lexicon. During the training phase, possible tags for a given token constitute the observable layer and the actual tag the hidden state. Information not relevant to the 28 A comprehensive account of the the tag set used in PAROLE can be found in Keson (1998b) and an abridged version in Keson (1998a).
6.4. Further reading 137 disambiguation process should not be given in the tags at this state. Similarly, during tagging, the word forms of the text in question are mapped to this simple tag-set. The design of the jaPOS tagger described in Chapter 7 focuses on this approach. 6.4 Further reading ⊲ The tag set applied in WP 2.1 as well as necessary modifications of the PAROLE training corpus is described in Chapter 7. ⊲ A detailed account of tokenization is given in Chapter 5. ⊲ For a description of the lexicon see Chapter 8.
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The Corpus Thread “Reference corp
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Chapter 1 Aim and concepts Sketchin
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1.1. Aim of the project 5 Metadata
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1.2. Project tasks and documentatio
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1.3. Text collection, text bank, co
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Chapter 2 The text bank A platform
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2.2. Implementation 17 and metadata
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Chapter 3 Text metadata What the he
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3.1. Concepts 21 Section 3.3 starts
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3.2. Header structure 23 (file des
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3.2. Header structure 25 great verb
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3.2. Header structure 27 The follow
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3.2. Header structure 29 deprecated
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3.2. Header structure 31 the text i
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3.2. Header structure 33 Another el
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3.2. Header structure 35 appDesc
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3.2. Header structure 37 informal p
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3.2. Header structure 39 3.2.3.4 Te
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3.3. Filling in the header 41 revi
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3.3. Filling in the header 43 samp
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3.3. Filling in the header 45 and 9
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3.3. Filling in the header 47 descr
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3.3. Filling in the header 49 ⊲ a
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3.3. Filling in the header 51 Legal
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3.3. Filling in the header 53 ⊲ a
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3.3. Filling in the header 61 Value
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3.3. Filling in the header 65 ⊲ p
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3.3. Filling in the header 67 Prope
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3.3. Filling in the header 83 3.3.3
Page 85 and 86: 4.1. Basic considerations 85 Outlin
Page 87 and 88: 4.2. Formatting text 87 russiske hi
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Page 95 and 96: 4.2. Formatting text 95 i dag . T
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Page 99 and 100: 5.1. Implementation 99 5.5.2 Implem
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Page 127 and 128: 6.1. Requirements 127 Outline of th
Page 129 and 130: 6.2. Survey 129 1. IMS’s TreeTagg
Page 131 and 132: 6.2. Survey 131 Code: Java. Assessm
Page 133 and 134: 6.2. Survey 133 9. alias-i’s Ling
Page 135: 6.3. Case study 135 lemmatization c
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Page 141 and 142: 7.2. The ePOS tag set for Danish 14
Page 153 and 154: Chapter 8 The full-form lexicon Sam
Page 155 and 156: 8.1. Enhancing existing material 15
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Page 159 and 160: Part V Deployment 159
Page 161 and 162: 9.2. Text material 161 Type Source
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Page 175 and 176: Bibliography Andersen, M. S., Asmus
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