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

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‘I am looking forward to being able to walk through the Brandenburg Gate for the first time.’ Figure 5: Example of grammatical function V-MOD The grammatical function V-MOD is used for verbal modifiers that are not obligatory. The sentence in Figure 5 illustrates this usage of durch (‘through’) as an optional modifier of the verb gehen (‘go’). Table 6 shows that the three grammatical functions (GF) just surveyed exhibit rather distinctive distributions for the subclasses of NE differentiated in the TüBa-D/Z. durch FOPP OPP V-MOD GF average ORG + PER 70.8% 14.3% 29.7% 49.3% GPE + LOC 12.5% 85.7% 62.2% 43.4% Table 6: Distribution of NE subclasses in prepositional phrases headed by durch Among the NEs that appear in prepositional phrases headed by durch, a large majority (70.8%) belongs to the NE subclasses of ORG and PER, when the prepositional phrase instantiates the grammatical function FOPP. This grammatical function is used for passive constructions. Therefore, the noun phrases that co-occur with durch in this construction have to have agent-like properties. Since such properties are typically associated with the two subclasses ORG and PER, this empirical finding is hardly surprising. On average, the subclasses of ORG and PER occur with the preposition durch under any of the examined grammatical functions (OPP, FOPP, PRED, V- MOD, MOD, Non-head ‘–’) with a relative frequency of 49.3%. This contrasts sharply with the relative frequency of 70.8% for the grammatical function FOPP. 22
The preposition durch typically has the directional sense of ‘through’, when it appears with the grammatical functions OPP and V-MOD. Since this directional sense is consistent with noun phrases that refer to physical locations and geo-political entities, the distribution of NEs in these syntactic environments is skewed toward these two subclasses of NEs (85.7% for OPP, and 62.2% for V-MOD). Note also, that these skewed distributions vary significantly from the average of 43.4%, with which these two subclasses of NEs occur with the preposition durch for any of the examined grammatical functions mentioned above. Similar facts hold for the preposition von, as Table 7 shows. Passivized subjects in von-phrases are annotated with the grammatical function FOPP; obligatory prepositional objects OPP often occur with noun phrases of NE subclasses ORG or PER. Verbal modifiers V-MOD, by contrast, contain more geographical modifiers and are more frequent than prepositional objects. von FOPP OPP V-MOD GF average GPE + LOC 12.8% 25.7% 38.5% 73.1% ORG + PER 85.1% 65.7% 59.0% 49.3% Table 7: Distribution of NE subclasses in prepositional phrases headed by von 5 Conclusion This paper has provided an overview of the NE annotation included in the TüBa-D/Z treebank of German and has described the subclasses of NEs distinguished by the TüBa-D/Z annotation scheme. Two types of linguistic features have been identified that show a high correlation with particular subclasses of NE in the TüBa-D/Z. Word co-occurrence features corroborate the effectiveness of the type of surface-oriented features used in state-of-theart sequence models for automatic NE resolution. In addition, syntax-based features have been described that go beyond the modeling currently possible with sequence models. These features demonstrate the utility of treebank annotations for the purpose of NE recognition and classification. Future directions of research include practical experiments with the TüBa- D/Z NE data, to the best of our knowledge the largest NE annotated data set available for German, with state-of-the-art NE resolvers as well as theoretical explorations how current sequence models for NE resolution can be extended so as to be able to accommodate the type of syntax-based features made available by NE annotations in treebanks such as the TüBa-D/Z. 23
Page 1 and 2: A Treebank-based Investigation of I
Page 3 and 4: participle and a (te-)infinitival c
Page 5 and 6: Some verbs occur twice, since they
Page 7 and 8: Profiling Feature Selection for Nam
Page 9 and 10: prepositional objects (FOPP, OPP).
Page 11 and 12: the limited size of annotated data
Page 13 and 14: with high precision typically have
Page 15: ‘This was “not significantly”
Page 19 and 20: Non-Projective Structures in Indian
Page 21 and 22: the sequential order of nodes in a
Page 23 and 24: extra-posed relative clause that ge
Page 25 and 26: Experiments on Dependency Parsing o
Page 27 and 28: for mitigating the effects of spars
Page 29 and 30: obtained with MALTParser is 76.61%
Page 31 and 32: as a standardised serialisation for
Page 33 and 34: constituency and dependency, possib
Page 35 and 36: SynAF and/or in . However, they sha
Page 37 and 38: shows how some elements that are no
Page 39 and 40: Example
Page 41 and 42: ‏ Example 8: represent
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Page 45 and 46: Chinese) as the direct object NP.
Page 47 and 48: Example 15: Tokens and Word Forms
Page 49 and 50: In a second experiment, a dataset w
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Page 53 and 54: [3] Leech G. N., Barnett, R. & Kahr
Page 55 and 56: Effectively long-distance dependenc
Page 57 and 58: In French, another case of eLDD is
Page 59 and 60: elativization, it-clefts or questio
Page 61 and 62: 4.2.3 Annotation methodology Becaus
Page 63 and 64: Number of occurrences in FTB +SEQTB
Page 65 and 66: 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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Analyzing the Most Common Errors in
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Graph 1 shows results of subsequent
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in the semantic type). This situati
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Will a Parser Overtake Achilles? Fi
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annotation is also based on a depen
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Set Name Sentences Tokens % Train/T
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dency relations (PRED, OBJ, SBJ, AD
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ton and Stacklazy), we trained a mo
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Feature Function Column Name Addres
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Using Parallel Treebanks for Machin
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SUJ NP SENT MOD PP NP PP PP NP NP P
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Checkpoint Instances Google PT Our
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6 Conclusions In this paper we have
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An integrated web-based treebank an
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Figure 2: Screenshot of the interfa
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ple subcategorization frames may be
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4 Integrated interface for annotati
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of the 5th International Conference
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Translational divergences and their
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posed so far, Cyrus’ work did not
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allowed an appropriate coverage of
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damentales [...] 10 (the universal
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for all lex_pair(s,t) do if head an
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the treatment of typical translatio
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Building a treebank of noisy user-g
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Phenomenon Attested example Std. co
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Figure 1: French Social Media Bank
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Impact of treebank characteristics
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Det Adj N (a) Danish Det Adj N (b)
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sv: Bestämmelserna i detta avtal f
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vidner , tilhørere og tiltalte wit
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mance is not inconsiderable, as was
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Genitives in Hindi Treebank: An Att
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A genitive can occur with complex p
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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