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

More documents

Recommendations

Info

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
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 and 166: posed so far, Cyrus’ work did not
Page 167 and 168: allowed an appropriate coverage of
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: Genitives in Hindi Treebank: An Att
Page 197 and 198: quite high. This motivates us towar
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?