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

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construction, we achieve a medium score. This result is encouraging because our Treebank has highest number of representation of this data. If such data can automatically be labeled for correct relation for most of the time, a lot of human labor and time will be saved. The overall result of right attachment and labeling is 89.35%. We note that predicting r6v relation where a genitive noun directly modifies a verb is very difficult because of the non-contiguous occurrence of the genitive modifier and the head in such instance. The main reason for this can be attributed to the greedy selection made by the rule based algorithm, in the sense that it will pick up the first context that one of the rule satisfies without verifying other contexts. For example, given the following sentence, rAm kA ghar jAnA. „Ram‟s going home‟, the system will connect rAm with ghar and assign r6 label without considering the possibility of ram‟s being connected to jAnA which would be the right attachment in this case. To handle this issue, we plan to use a technique that considers all the candidates for head and selects the most probable candidate as the head. 7 Conclusion The paper presents a detailed study of genitive data in Hindi Treebank. We have examined Hindi dependency Treebank and noted down various syntactico-semantic relations in which genitive noun occurs with respect to its head. We have attempted to trace syntactic contexts which can be used for predicting the relations automatically. The motivation is to automate the process of labeling genitive data. The rule based system implemented in the paper works quite well and predicts the syntactic contexts to a great extent. The output can be verified by the human annotators thus making the Treebank development semi-automatic for the genitive data. References [1] A. Bharati, D. M. Sharma, S. Husain, L. Bai, R. Begam and R. Sangal. 2009. AnnCorra: TreeBanks for Indian Languages, Guidelines for Annotating Hindi TreeBank (version – 2.0). [2] R. Girju, (2008) Tutorial on semantic relation extraction and its applications. Proceedings of European Summer School in Logic, Language and Information (ESSLLI), Freie und Hansestadt Hamburg, Germany [3] M. Gupta, V. Yadav, S. Husain and D. M. Sharma. 2008. A Rule Based Approach for Automatic Annotation of a Hindi Treebank. 6th International Conference on Natural Language Processing (ICON-08), CDAC Pune, India. [4] R. Bhatt, B. Narasimhan, M. Palmer, O. Rambow, D. M. Sharma and F. Xia. 2009. Multi-Representational and Multi-Layered Treebank for Hindi /Urdu. In Proc. of the Third Linguistic Annotation Workshop at 47th ACL. [5] Sapna Sharma (2012). Disambiguating the Parsing of Hindi Recursive Genitive Constructions. MS Thesis. IIIT Hyderabad, India 204
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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.
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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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[9] G. Doddington, A. Mitchell, M.
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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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phrases are generated by a shallow
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Figure 2: A Kybot p
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SUJ NP SENT MOD PP NP PP PP NP NP P
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Page 149 and 150: 6 Conclusions In this paper we have
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Page 163 and 164: Translational divergences and their
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Page 175 and 176: Building a treebank of noisy user-g
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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
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Page 193 and 194: Genitives in Hindi Treebank: An Att
Page 195 and 196: A genitive can occur with complex p
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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