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

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e of great help and that the presence of a set of rules for the detection of translational correspondences can, if not replace, significantly complement the work done by statistical systems. Linguistic knowledge can be of help in identifying not only the exact matches, but also (we would rather say in particular) all those cases in which there are partial or fuzzy correspondences due, for example, to the individual translator choices or to differences - which often occur in a systematic way - between language pairs. In this paper we focus our attention especially on these differences (which we will designate with the term "shift"), on their classification, and finally on a proposal for their automatic processing. We start, in Section 2, from the discussion of some works in the field of Translation Studies where reference is made to the notion of translation shift, and we then present an existing resource, i.e. ParTUT, which has been used as the basis for our analysis. The remainder of the paper is structured as follows: in Section 3 we provide a brief description of the corpus content and of the annotation format of the treebank, while Section 4 is devoted to the cases of translation shifts encountered so far in the corpus and to the identification of the main points our research should be addressed to. 2 Related Work Due to the peculiarities of each language system, translation process may be quite complex and correspondences could be drawn up on various linguistic levels: lexical, structural or semantic; it therefore entails the need to implement strategies (or “shifts”) so that the meaning is properly transposed and preserved in such process. John Catford [1] was the first one to use the term shift, which he defined as the departure from formal correspondence in the process of going from the source language to the target language. His definition relied upon the notions of formal correspondence and textual equivalence. A formal correspondence is a relationship between two linguistic items that play the same role in their respective systems, while textual equivalence is defined, in a broader sense, as any kind of translational equivalence in a pair of texts or sentences. A shift then occurs in translation whenever there is not a formal correspondence relationship between two elements in source and target texts. In his work, Catford discussed two types of shift: level and category shift. The former deals with shifts between different linguistic levels (usually grammar and lexis), while category shift is further subdivided in: class shift (e.g. in the word class used), unit or rank shift (e.g. when a single word is translated by means of a phrase), structural shift (e.g. when word order is modified) – this is considered the most frequent among the category shifts – and intra-system shift (i.e. when, despite the presence of a formal correspondence between source and target elements, a non-corresponding form is chosen while translating). An important contribution which largely relied upon the linguistic theories described above is that of Cyrus [3], who explicitly annotated translation shifts in a parallel treebank. Contrarily to most of the works on parallel treebanks pro- 170
posed so far, Cyrus’ work did not aim at creating a machine translation system, but at building a resource for English and German (FuSe) in which translation shifts were explicitly annotated on the basis of the predicate-argument structure. The annotation system was based on the distinction between two main classes, i.e. grammatical and semantic shifts. In the first one, all those cases of passivisation– depassivisation, pronominalisation–depronominalisation, as well as category and number changes were included. Semantic shifts comprised the cases when meaning is somewhat involved and were classified as follows: semantic modification, explicitation– generalisation, addition–deletion and mutation. These works have been an important theoretical reference for our research. The analysis of translational correspondences and divergences made on our small set of sentences, besides taking into account the observed cases, as well as some peculiarities of the corpus (such as the annotation format), has been largely inspired by such works in its theoretical formalization and systematization. 3 Corpus description 3.1 Data ParTUT 1 currently comprises 42,347 tokens distibuted as shown in Table 1. The corpus consists of an average amount of 465 sentences per language. They were retrieved from the JRC-Acquis multilingual parallel corpus 2 [15] and the entire text (about 100 sentences) of the Universal Declaration of Human Rights 3 . More recently this preliminary set has been enlarged with an additional corpus extracted from the open licence “Creative Commons” 4 composed by around 100 sentences, and from publicly available pages from Facebook website. The corpus is aligned on the sentence level with the Microsoft Bilingual Sentence Aligner ([12]) and the LF Aligner 5 , an automatic tool based on Gale and Church algorithm which enables the storage of sentence pairs as translation units in TMX files and the review of the output in formatted xls spreadsheets. 3.2 The annotation format The parallel treebank comprises a collection of sentences represented in the form of dependency structures. The dependency relations are described in compliance with the same criteria adopted for the creation of the Italian monolingual treebank TUT (Turin University Treebank) 6 . 1 http://www.di.unito.it/˜tutreeb/partut.html 2 http://langtech.jrc.it/JRC-Acquis.html, http://optima.jrc.it/Acquis/ 3 http://www.ohchr.org/EN/UDHR/Pages/SearchByLang.aspx 4 http://creativecommons.org/licenses/by-nc-sa/2.0 5 http://sourceforge.net/projects/aligner/ 6 http://www.di.unito.it/˜tutreeb/ 171
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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.
Page 113 and 114: citizens of Bilbao] are very involv
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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
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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: Translational divergences and their
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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
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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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