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

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# sent. # tokens avg. lgth std dev. noisiness score DOCTISSIMO 771 10834 14.05 10.28 0.37 high noisiness subcorpora 36 640 17.78 17.63 1.29 other subcorpora 735 10194 13.87 9.74 0.31 JEUXVIDEOS.COM 199 3058 15.37 14.44 0.81 TWITTER 216 2465 11.41 7.81 1.24 high noisiness subcorpora 93 1126 12.11 8.51 1.46 other subcorpora 123 1339 10.89 7.20 1.08 FACEBOOK 452 4200 9.29 8.17 1.67 high noisiness subcorpora 120 1012 8.43 7.12 2.44 other subcorpora 332 3188 9.60 8.49 1.30 Table 1: Corpus properties French linguistic modeling and statistical parsing, were obtained by looking for slang words and urban idiomatic constructions. Table 1 presents some properties of our corpora. In order to quantitatively assess the level of noisiness in our corpora we defined an ad-hoc noisiness metric. It is defined as a variant of the Kullback–Leibler divergence between the distribution of trigrams of characters in a given corpus and that in a reference corpus (in our case, the French Treebank (Abeillé et al. [1]), hereafter FTB). The figures given in Table 1 are consistent with our classification in two noisiness levels. We used this metric to decide for each sub-corpus whether to apply a standard pre-annotation or a dedicated noise-tolerant architecture instead (cf. Section 5). We refer the reader to (Seddah et al. [13]) for more detail on our various subcorpora. We provide here two examples, (1) from the lightly edited TWITTER subcorpus, and (2), from the our high-noisiness FACEBOOK subcorpus. (1) Je soupçonnes que "l’enfarineuse" était en faite une cocaineuse vu la pêche de #Hollande ce soir à #Rouen. Je soupçonne que l’enfarineuse était en fait une cocaïneuse vu la pêche de #Hollande ce soir à #Rouen. I suspect that the “flouring-lady” was actually a cocaïn-lady given the energy of #Hollande this night at #Rouen. (2) L’Ange Michael vraiment super conten pour toi mé tora plus grace a moi tkt love you! L’Ange Michael: (Je suis) Vraiment super content pour mais tu auras plus grace à moi. Ne t’inquiètes pas. Je t’aime ! The Angel Mickael: (I am) Really very happy for him but you’ll get more because of me. Don’t worry. I love you! 3 Linguistics of user generated content User-generated texts do not correspond to a single homogenous domain, although some specificities of user-generated content are found across various types of web data. Moreover, in some cases, and most notably TWITTER, such data include both linguistic content and media-specific meta-language. This meta-language (such as 182
Phenomenon Attested example Std. counterpart Gloss ERGOGRAPHIC PHENOMENA Diacritic removal demain c’est l’ete demain c’est l’été ‘tomorrow is summer’ Phonetization je suis oqp je suis occupé ‘I’m busy’ Simplification je sé je sais ‘I know’ Spelling errors tous mes examen tous mes examens ‘All my examinations son normaux sont normaux are normal’ TRANSVERSE PHENOMENA Contraction nimp n’importe quoi ‘rubbish’ qil qu’il ‘that he’ Typographic diaeresis c a dire c’est-à-dire ‘namely’ c t c’était ‘it was’ MARKS OF EXPRESSIVENESS Punctuation transgression Joli !!!!!! Joli ! ‘nice!’ raphemic stretching superrrrrrrrr super ‘great’ Emoticons/smileys :-),
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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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The Effect of Treebank Annotation G
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ut without feature structures. This
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only the lexicon is fine-grained to
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Automatic Coreference Annotation in
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manually annotated Czech sentences.
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citizens of Bilbao] are very involv
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4.1.3 Coreference Selector Module T
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Nominal P R F1 MUC 75.33% 81.33% 78
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[9] G. Doddington, A. Mitchell, M.
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Analyzing the Most Common Errors in
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Graph 1 shows results of subsequent
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Page 129 and 130: annotation is also based on a depen
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Page 149 and 150: 6 Conclusions In this paper we have
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Page 163 and 164: Translational divergences and their
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Page 179 and 180: Figure 1: French Social Media Bank
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Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
Page 185 and 186: sv: Bestämmelserna i detta avtal f
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Page 193 and 194: Genitives in Hindi Treebank: An Att
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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