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

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meantime under the code name , to comply with recent XML technology developments 2 , to integrate some necessary features and to fully encompass dependency-based representations. Before proceeding any further with this initiative, the SC4 experts who attended the ISO TC37 meeting in Madrid in June 2012 decided to put to the test, by applying it to a variety of language samples. This should provide a concrete basis for the future committee work, which should integrate the feedback from the community interested in syntactic annotation and in particular colleagues involved in the development of treebanks. In this context, this paper is intended to provide a first overview of these experiments, with the aim of raising some interest and thereby getting some feedback from a variety of interested experts. This paper will first of all describe the major characteristics of both the ISO SynAF standard and the formats and then present a series of short sections demonstrating a) the possible application of in specific linguistic contexts; and b) how SynAF can be used as an interchange format for existing tools. As mentioned, this paper discusses work in progress. Consequently, we expect it to lead other colleagues to start experimenting with and, thus, provide some concrete feedback to the forthcoming standardisation work. 2 The SynAF metamodel ISO 24615 (SynAF) provides a high-level metamodel for representing the syntactic annotation of linguistic data, with the objective of supporting interoperability across language resources or language processing components. SynAF aims at covering the two main functions of syntactic annotation in language processing, namely: a) to represent linguistic constituency, as in Noun Phrases (NP) etc., describing a structured sequence of morpho-syntactically annotated items (including, depending on the theory used, empty elements or traces generated by movements at the constituency level), as well as constituents built from non-contiguous elements; and b) to represent dependency relations, such as head-modifier relations, and also including relations between categories of the same kind (such as the headhead relations between nouns in appositions, or nominal coordinations in some formalisms). As a consequence, syntactic annotations must comply with a multi-layered annotation strategy, which interrelates syntactic annotation for both 2 Typically: generic XML attributes, schema technology, XML design good practices. Although it is not the aim of this paper to go in to the corresponding technical details, we encourage interested readers to refer to the documentation 38
constituency and dependency, possibly simultaneously, as stated in the SynAF metamodel. The metamodel is based on the following components: • The T_node component represents the terminal nodes of a syntactic tree, consisting of morpho-syntactically annotated word forms, as well as empty elements when appropriate. T_nodes are annotated with syntactic categories valid for the word level. • The NT_node component represents the non-terminal nodes of a syntactic tree. The NT_nodes are annotated with syntactic categories that are valid at the phrasal, clausal and/or sentential levels. • The Edge component represents a relation between syntactic nodes (both terminal and non-terminal nodes). For example, the dependency relation is binary, consisting of a pair of source and target nodes, with one or more annotations. From this metamodel, a specific syntactic annotation model can be obtained by combining the above-mentioned components with data categories characterising or refining their semantics. It should be noticed that the terminal node level in SynAF is strongly equivalent to the word form level in MAF (ISO 24611 [5]), for which we offer concrete interfaces below. It is thus left to the implementer to either separate or merge these two components depending on whether it is relevant or not, for instance, to clearly differentiate the data categories attached to word forms and terminals within a multi-layered annotated corpus. 3 as a SynAF compliant Tiger The main characteristics of the datamodel can be summarized as follows: • Terminal nodes are implemented as elements, either referring to a textual segment or pointing to a word form in a morphosyntactically annotated corpus. Non-terminal nodes are implemented as elements and are used to represent hierarchical structures like syntactic trees • The Edge component is implemented by means of an element, which may relate either or elements to each other. • A generic @type attribute inside terminal, non-terminal and edge elements can further qualify the meaning of these elements. For instance a non-terminal node can be qualified as a verbal complex, a terminal can be specified as a compound node etc. • The elements terminal, non-terminal and edge as well are used as placeholders for further linguistic annotations. Such annotations can freely be added as generic attribute-value-pairs with no restrictions to 39
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 and 16: ‘This was “not significantly”
Page 17 and 18: The preposition durch typically has
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: as a standardised serialisation for
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
Page 43 and 44:
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
Page 51 and 52:
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
Page 67 and 68: Logical Form Representation for Lin
Page 69 and 70: gerundives for a total amount of so
Page 71 and 72: object+indirect object/object one.
Page 73 and 74: (VP (VB patch) ) ) ) (VP (VBZ is) (
Page 75 and 76: Types Adverb. Adject. Verbs Nouns T
Page 77 and 78: Eventually we may comment that ther
Page 79 and 80: DeepBank: A Dynamically Annotated T
Page 81 and 82: from another already existing one,
Page 83 and 84:
to parse now does. The extra manual
Page 85 and 86:
In the derivation show in Figure 1,
Page 87 and 88:
4 Patching Coverage Gaps with An Ap
Page 89 and 90:
will enable further improvements in
Page 91 and 92:
ParDeepBank: Multiple Parallel Deep
Page 93 and 94:
2 The ParDeepBank The PTB has emerg
Page 95 and 96:
undergone extensive scientific scru
Page 97 and 98:
the second combines the structures
Page 99 and 100:
Sofia University). Each sentence wa
Page 101 and 102:
data, and improvements in the infra
Page 103 and 104:
The Effect of Treebank Annotation G
Page 105 and 106:
ut without feature structures. This
Page 107 and 108:
only the lexicon is fine-grained to
Page 109 and 110:
Automatic Coreference Annotation in
Page 111 and 112:
manually annotated Czech sentences.
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
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6 Conclusions In this paper we have
Page 151 and 152:
An integrated web-based treebank an
Page 153 and 154:
16] and we have earlier reported fr
Page 155 and 156:
Figure 2: Screenshot of the interfa
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
Page 167 and 168:
allowed an appropriate coverage of
Page 169 and 170:
damentales [...] 10 (the universal
Page 171 and 172:
for all lex_pair(s,t) do if head an
Page 173 and 174:
the treatment of typical translatio
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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