A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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Figure 1: Screenshot showing OCR-read text in the preprocessing interface heading. Another common cause of sentences being incorrectly marked as headings is missing end punctuation, a not uncommon OCR error with some fonts. For each paragraph, a number of buttons provide the most common correction choices: deleting one or more paragraphs, linking paragraphs, editing within a paragraph, adding missing punctuation, marking a paragraph as a title or marking a presumed title as a paragraph. The [Show] button causes the scanned image of the text to appear on the interface; this is useful when it is necessary to check the original. Unknown words are marked in boldface and shaded with a yellow background in the text, making them easy for the annotator to spot. In figure 1 this is the case with the string plasert, an archaic spelling of plassert ‘placed’. All unknown words are not only highlighted in the text, but are also presented in an alphabetical list, as shown at the top of figure 2. There is also a list of extracted uncertain (suspicious) words, e.g. the numeral 10, which is sometimes produced by the OCR software instead of the intended word lo ‘laughed’. The lists of unknown and uncertain words will shrink when the initial text cleanup is done, so that they only display words that need to be further categorized. 3.2 Categorizing unknown words Foreign words, named entities, productive compounds, multiword expressions, neologisms, misspellings and variant spellings are examples of the types of words that may not be recognized automatically in the initial preprocessing phase and that need to be categorized. The interface for this processing step is shown in figure 2, where the selection of the string Bergom has opened the Store as window, in which the annotator can make appropriate choices to correct the word or add it to the lexicon. Each occurrence of a chosen word in the text is shown with its immediate context in the Context(s) window. In the case of the string Bergom, the contexts make it
Figure 2: Screenshot of the interface for working with unrecognized words clear that this is a family name, since in both contexts it is preceded by the word Fru ‘Mrs.’. For each context, there is a hyperlink to the position of the occurrence in the running text so that the string may be viewed in an even broader context. An additional hyperlink will display the page in the photographic scan of the original, which sometimes reveals that characters are missing in the OCR version. Typos and OCR errors can be systematically corrected in this interface, even though they can also be edited directly in the text interface in figure 1. Variant spellings that could be common, such as the earlier mentioned plasert, may be added to the morphology since we would like to be able to parse the sentence even though the word has a nonstandard orthography. Such spellings also get a special tag in the morphology so that they are marked as deviant; we would not want to produce these forms if the grammar were to be used for generation. The context will sometimes suggest that the word is part of a multiword expression. The annotator can then select adjacent words to be added as a fixed multiword entity. Not all multiwords may be treated in this way, of course; some require special treatment in both the lexicon and the grammar. Identifying multiword entities is important with respect to parsing, since those unrecognized by the morphology will be analyzed compositionally by the parser unless they have already been entered into the LFG lexicon. Not all OCR, typographical and spelling errors are captured by morphological preprocessing. This may be for instance because the string is an existing word. For example, when the word form term ‘term’ occurs in the context term lyset ‘term the light’, it is clear to a native speaker that the original text must have read tenn lyset ‘light the light’. This may be confirmed by consulting the image of the original text. Such errors may be spotted both during text cleanup and during further word
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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‏ Example 8: represent
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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elativization, it-clefts or questio
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4.2.3 Annotation methodology Becaus
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Number of occurrences in FTB +SEQTB
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producing treebank based LFG approx
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Logical Form Representation for Lin
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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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undergone extensive scientific scru
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the second combines the structures
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Sofia University). Each sentence wa
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data, and improvements in the infra
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Page 105 and 106: ut without feature structures. This
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Page 113 and 114: citizens of Bilbao] are very involv
Page 115 and 116: 4.1.3 Coreference Selector Module T
Page 117 and 118: Nominal P R F1 MUC 75.33% 81.33% 78
Page 119 and 120: [9] G. Doddington, A. Mitchell, M.
Page 121 and 122: Analyzing the Most Common Errors in
Page 123 and 124: Graph 1 shows results of subsequent
Page 125 and 126: in the semantic type). This situati
Page 127 and 128: Will a Parser Overtake Achilles? Fi
Page 129 and 130: annotation is also based on a depen
Page 131 and 132: Set Name Sentences Tokens % Train/T
Page 133 and 134: dency relations (PRED, OBJ, SBJ, AD
Page 135 and 136: ton and Stacklazy), we trained a mo
Page 137 and 138: Feature Function Column Name Addres
Page 139 and 140: Using Parallel Treebanks for Machin
Page 141 and 142: phrases are generated by a shallow
Page 143 and 144: Figure 2: A Kybot p
Page 145 and 146: SUJ NP SENT MOD PP NP PP PP NP NP P
Page 147 and 148: Checkpoint Instances Google PT Our
Page 149 and 150: 6 Conclusions In this paper we have
Page 151 and 152: An integrated web-based treebank an
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Page 157 and 158: ple subcategorization frames may be
Page 159 and 160: 4 Integrated interface for annotati
Page 161 and 162: of the 5th International Conference
Page 163 and 164: Translational divergences and their
Page 165 and 166: posed so far, Cyrus’ work did not
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Page 171 and 172: for all lex_pair(s,t) do if head an
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Page 175 and 176: Building a treebank of noisy user-g
Page 177 and 178: Phenomenon Attested example Std. co
Page 179 and 180: Figure 1: French Social Media Bank
Page 181 and 182: Impact of treebank characteristics
Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
Page 185 and 186: sv: Bestämmelserna i detta avtal f
Page 187 and 188: 100 90 80 Unlabelled attachment 70
Page 189 and 190: vidner , tilhørere og tiltalte wit
Page 191 and 192: mance is not inconsiderable, as was
Page 193 and 194: Genitives in Hindi Treebank: An Att
Page 195 and 196: A genitive can occur with complex p
Page 197 and 198: quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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