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

More documents

Recommendations

Info

Corpus sentences tokens JRCAcquis–It 181 6,304 JRCAcquis–En 179 4,705 JRCAcquis–Fr 179 8,667 UDHR–It 76 2,387 UDHR–En 77 2,293 UDHR–Fr 77 2,537 CC–It 96 3,141 CC–En 88 2,507 CC–Fr 102 3,624 FB–It 115 1,947 FB–En 114 1,723 FB–Fr 112 2,512 total 1,377 42,347 Table 1: Corpus overview. As far as the native annotation schema is concerned, a typical TUT tree shows a pure dependency format centered upon the notion of argument structure and is based on the principles of the Word Grammar theoretical framework [7]. This is mirrored, for instance, in the annotation of Determiners and Prepositions which are represented in TUT trees as complementizers of Nouns or Verbs. By contrast, the native TUT scheme exploits also some representational tools, i.e. null elements (which are non–standard in dependency-based annotations), in order to deal with particular structures, such as pro–drops, long distance dependencies and elliptical structures. As for the dependency relations that label the tree edges, TUT exploits a rich set of grammatical items designed to represent a variety of linguistic information according to three different perspectives, i.e. morphology, functional syntax and semantics. The main idea is that a single layer, the one describing the relations between words, can represent linguistic knowledge that is proximate to semantics and underlies syntax and morphology, i.e. the predicate-argument structure of events and states. To this end, a distinction is drawn between modifiers and subcategorized arguments on the one hand and between surface and deep realization of any admitted argument; these annotation criteria have proven particularly useful while detecting cases of nominalisations and passivisation, which are common cases of divergence in translation. In a cross-linguistic perspective, the choice to use a single and coherent representation format, and the TUT format in particular, proved to be suitable in the development of a parallel resource in that the rich morphological tag set allowed an adequate representation for both morphologically rich languages (Italian and French) and simpler ones (English), and the richness and flexibility of relations 172
allowed an appropriate coverage of new linguistic phenomena encountered so far. Such format proved also to be useful in comparative analyses, by means of which some typical phenomena of the three languages involved could be easily queried and quantified. Among these we can find the higher frequency of nominal modifiers (4.5%) in English texts (expressed by the NOUN-RMOD label), with respect to Italian (0.9%) and French (0.6%), or, on one hand, the presence (marked, as explained above, by null elements) of pro-drops in Italian sentences (56 occurrences against one single entry in English and the absence in French) and, on the other, the use of expletive subjects in English and French (respectively 19 and 21 occurences). Furthermore, the annotation of Determiners as complementizers of Nouns (as also pointed out above) and the lower frequency of determiners in English (11.6%, compared to 13.4% in French and 16.1% in Italian) led to a different representation of English trees with respect to Ialian. These preliminary considerations, together with the absence of an appropriate tool that allows the alignment of sentences represented according to the TUT format, have led to the decision of working on the development of a new system. As, in fact, the choice of the aligment tool is strongly related to the final task, we cannot abstract away from the input format, especially if it provides linguistically relevant information which can be useful for any further corpus processing and exploitation. 4 Alignment issues In this section we describe the main steps of the investigation which has lead us to the definition of our approach for the alignment of the trees in ParTUT. As a first step, we selected a small subset of sentences from the collection and attempted to align them manually, in order to specifically study the various issues that could arise, and in particular translation shifts. We selected for that purpose the first two subcorpora of the treebank, i.e. the JRC-Acquis and the UDHR, which were already aligned on the sentence level. In order to avoid further drawbacks deriving from multiple or null correspondences – which were caused in some cases by the different segmentation criteria adopted by the parser and the sentence aligner – we only worked on 1:1 sentence pairs (constituting almost the 90% of the overall amounts of sentence pairs). In particular, the experimental corpus was composed by 50 sentences per language divided into three pairs (i.e. Italian - English, English - French, French - Italian). While comparing the tree pairs, we observed the various cases of divergences, or shifts, and attempted to classify them. Similarly to what proposed in [3], we identified two main classes of shifts, each one involving respectively morpho-syntactic and structural level on one hand, and semantic level on the other. In the first class we include: Category shift 7 : when different parts of speech are used in source and target text. 7 Although Catford used this term for describing a main class of shifts that included other sub- 173
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 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
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
Page 149 and 150: 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: posed so far, Cyrus’ work did not
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?