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

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2 Genitive Data in Hindi In Hindi, case markers occur as postpositions. The default genitive case marker specifies a relation between two nouns: head and modifier as in rAm kA ghar (Ram’s house), where rAm modifies the head ghar. The genitive case marker is kA, which has allomorphic variations as kI and ke, governed by the grammatical features of the following head as illustrated in Table 1: Allomorph Head Gram. feature Example kA Masculine, Singular, Direct Case rAm kA ghar „Ram‟s house‟ ke Masculine, Singular, Oblique Case saMwAdAtA ke savAl kA javAb diyA „Answered the question of Press‟ Masculine, Plural, Any congress kI nIiyAm „Policies of Congress‟ kI Feminine, Any brahaspativAr kI rAt „Thursday‟s night‟ Table 1: Allomorphic variation of the genitive marker in Hindi As has been widely studied by Girju (2008), the genitive marker between two nouns is highly polysemous in nature as they express different semantic relations. Hindi is no exception in this regard. However, the interesting fact of Indo-Aryan languages and other language families is that genitive data occur in many other contexts. We discuss those contexts in Hindi here one by one. The most significant one is the relation that occurs between the genitive noun and verb as illustrated in (1), which is distinct from (2), which is a regular noun-noun genitive construction. 1. rAm ke do beTA hE Ram-gen two son be-3sg pr „Ram has got two sons.‟ 2. rAm ke do beTe skul jA rahe hE Ram-gen two son school go be-3sg pr „Two sons of Ram are going to school.‟ In (1), the genitive noun is connected to the verb directly and not with the following noun do beTA „two sons‟. One might argue for the whole NP rAm ke do beTe „Two sons of Ram‟ to be argument of the verb hE „is‟ in the sense of „There exists two sons of Ram‟. This interpretation is not viable because the NP do beTe can be scrambled with hE as in (3), which is not a regular phenomenon for Noun-gen Noun construction. 3. rAm ke hE do beTe, aur madhu ke tin Ram-gen be-3sg pr two sons and Madhu-gen three „Ram has got two sons and Madhu three.‟ However, the case becomes more complex than it was assumed in the beginning because we have come across instances where the head noun is not contiguous with its genitive modifier as exemplified in (4): 4. mAntriyoM kI samiti kI adyakshaytA rakshA mantrI karenge minister-pl-gen committee-gen chair defense minister do-3sg fut „The committee of the ministers will be chaired by Defense Minister.‟ 200
A genitive can occur with complex predicate, which is composed of one noun or adjective and a light verb. For example, pratIkshA karnA in (5) is a complex predicate because it is a multiword expression denoting single event: 5. rAm sItA kI pratIkshA kar rahA thA Ram Sita-gen wait do be-3pr pst „Ram was waiting for Sita.‟ 6. rAm kA jAnA sambhav nahI hE rAm-gen go-VN possible neg be-3pr „It is not possible for Ram to go.‟ An argument of a verb regularly takes genitive in the context of verbal noun form of a verb. In (6), rAm is an argument of the verb jA „go‟. The same holds even when some participants intervenes the two as illustrated in (7). Another significant occurrence of genitive is when the head is elided as in (8): 7. rAm kA sItA ke sAth jAnA sambhav nahI hE rAm-gen Sita with go-VN possible neg be „It is not possible for Ram to go with Sita.‟ 8. yaha khAnA kal kA hE This food yesterday-gen be-3pr „This food is yesterday‟s (food).‟ We have examined various distributions of genitive data in Hindi. Table 2 attempts to tabulate all types of genitive that we have discussed in this section: CASE CONSTRUCTION TYPE EXAMPLE Case 1 Noun gen – Noun rAm kA ghar „Ram‟s house‟ Case 2 Noun gen – Verb rAm kA ek beTA hE „Ram has one son‟ Case 3 Noun gen – Complex predicate rAm sItA kI pratIkshA kar rahA thA „Ram was waiting for Sita‟ Case 4 Noun gen – Verbal Noun rAm kA jAnA „Ram leaving‟ Case 5 Noun gen – Head elided yaha khAnA kal kA hE „This (food) is yesterday‟s food‟ Table 2: Different type of genitive data in Hindi 3 Brief Description of Hindi Treebank The Hindi-Urdu dependency Treebank is being developed following the analysis of the Paninian grammatical model (Bharati et.al. 2009). As observed in Bhatt et al. (2009), “the model offers a syntactico-semantic level of linguistic knowledge with an especially transparent relationship between the syntax and the semantics.” The dependency relations are of two types: kAraka and non-kAraka. Karaka relations indicate the roles that various participants play with respect to a verb. There are six kAraka relations: karta, karma, karana, apadana, sampradana and adhikarana. kAraka relations capture one very significant semantic-pragmatic information which is known as vivakshA that can be translated as „speaker‟s choice‟. Sentences are treated as a series of chunks each having a head and one or more optional modifier of the head as shown below. The head of each chunk is highlighted. 201
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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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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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