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

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Also, it interacts with external resources and NLP tools. It is well suited for dependency graphs but its visualization does not seem to display constituent trees or (recursive) feature-value graphs, nor does it seem to support parse selection. In the INESS project, we have probably been the first to develop a fully webbased infrastructure for treebanking. This approach enables annotation as a distributed effort without any installation on the annotators’ side and offers immediate online exploration of treebanks from anywhere. In earlier publications, various aspects of this infrastructure have been presented, but specific interface aspects of the annotation process have not been systematically described. In the remainder of this paper, we will describe some aspects of the annotation interface that we have implemented. Section 2 provides an overview of the INESS project and reviews some of the web-based annotation features that have been described in earlier publications. After that we present new developments: in section 3 we discuss the preprocessing of texts to be parsed in the Norwegian treebank, and in section 4 the integrated issue tracking system is presented. 2 The INESS treebanking infrastructure INESS, the Norwegian Infrastructure for the Exploration of Syntax and Semantics, is a project cofunded by the Norwegian Research Council and the University of Bergen. It is aimed at providing a virtual eScience laboratory for linguistic research [19]. It provides an environment for both annotation and exploration and runs on its own HPC cluster. One of the missions of the INESS project is to host treebanks for many different languages (currently 24) and annotated in various formalisms, in a unified, accessible system on the web. Some treebanks are currently only small test suites, while others are quite large, for instance the TIGER treebank, which consists of about 50,000 sentences of German newspaper text. There are dependency treebanks, constituency treebanks, and LFG treebanks; furthermore a number of parallel treebanks are available, annotated at sentence level and experimentally at phrase level [10]. We have implemented middleware for online visualization of various kinds of structures and for powerful search functionality in these treebanks [14]. The second mission of the INESS project is to develop the first large treebank for Norwegian. This treebank is being built by automatic parsing with an LFG grammar, NorGram [8, 9], on the XLE platform [13]. An LFG grammar produces two parallel levels of syntactic analysis; the c(onstituent)-structure is a phrase structure tree, while the f(unctional)-structure is an attribute–value matrix with information about grammatical features and functions [4]. Deep analysis with a large lexicon and a large grammar can often lead to massive ambiguity; a sentence may have hundreds or thousands of possible analyses. Therefore, the LFG Parsebanker was developed in the LOGON and TREPIL projects to enable efficient semiautomatic disambiguation [17, 21]. We have developed discriminants for LFG analyses, as described earlier [20]. In our ongoing annotation work with the LFG Parsebanker, we are basically developing the grammar, the lexicon and a gold standard treebank in tandem [18,
16] and we have earlier reported from this interactive mode of development [12]. In this work it has become clear to us that a tightly integrated development of the grammar, the lexicon and the treebank requires new interactive tools in an integrated annotation environment. Therefore in the current paper we report on the design and implementation of this web-based environment for annotation, focusing on two components which deserve more attention: text preprocessing and issue tracking for treebanking. 3 Text preprocessing In any treebank of considerable size, identification of words unknown to the lexicon and/or morphology presents a challenge. Although INESS has access to a large lexicon of Norwegian and a sophisticated morphological component, our experience from parsing is that many words in naturally occurring texts are still unknown. Although statistical approaches may attempt to provide the missing lexical information, these are not applicable in our current approach to building a quality treebank, where one incorrectly analyzed word may be enough to cause the parser to fail to produce the correct analysis, even though the syntactic constructions involved in the sentence are within the coverage of the grammar. We have therefore implemented a text preprocessing interface that makes it possible for annotators to quickly identify unknown words and add the necessary properties for treebanking. An important source of texts for the INESS Norwegian treebank are OCR files from the National Library of Norway. The OCR software makes some errors, such as misinterpreting characters, omitting material, or inserting unwanted material into the text. These problems require efficient preprocessing, which we have implemented in two main stages: initial text cleanup and the treatment of unknown words. These will be discussed in the following sections. 3.1 Initial text cleanup For the initial text cleanup, the text preprocessing interface presents the text by paragraphs with editing functions. All material that does not belong to the running text itself, such as author, publisher, table of contents, and page numbers, must be removed in text cleanup. The screenshot in figure 1 shows a text from a book where the last sentence on a page continues on a new page with an illustration in between. The horizontal lines mark the borders between paragraphs. The word små ‘small’ towards the end of the second paragraph and the word skolepulter ‘classroom desks’ at the beginning of the last paragraph are consecutive words in the sentence broken up by the page break. The intervening illustration has caused the insertion of two unnecessary paragraphs with superfluous characters. The system hyphothesizes that paragraphs that do not end with punctuation are headings, and it marks such paragraphs with background shading. The shading makes it easy for the annotator to check these paragraphs to see if they are indeed headings. In this case, incorrectly split text is the cause of the incorrect marking as a
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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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Page 109 and 110: Automatic Coreference Annotation in
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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
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: An integrated web-based treebank an
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
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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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