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

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haemagglutinate, hemagglutinate cause the clumping together (VP (TO to) (VP (VB cause) (NP (DT the) (ADJP (VBG clumping) (RB together) ) ) haemagglutinate:VB(e1, x1, x2) -> cause:VB(e1, x1, e2) clump:VB(e2, x4, x2) together:RB(e2) set_ablaze, set_aflame, set_on_fire, set_afire set fire to; cause to start burning (VP (TO to) (VP (VB cause) (S (VP (TO to) (VP (VB start) (S (VP (VBG burning) ) ) set_ablaze:VB(e1, x1, x2) -> cause:VB(e1, x1, x2) to:IN(e1, e2) start:VB(e2, x2, e3) burn:VB(e3, x2, x3) shame cause to be ashamed (VP (VB cause) (S (VP (TO to) (VP (VB be) (ADJP (JJ ashamed) ) ) shame:VB(e1, x1, x2) -> cause:VB(e1, x1, x4) to:IN(e1, e2) be:VB(e2, x4, x3) ashamed:JJ(x3) Apart from the first example, HAEMAGGLUTINATE, which is correctly encoded, the two other cases are totally wrong. In particular, SHAME, contains a free variable X2, which is at first substituted by X4 and then by X3. In the tables here below I analyse the data related to the encoding of MAKE and CAUSE in LFs. As shown in Table 2., the use of MAKE in the 3PP format constitutes approximately 12% (457) of the total 3351 occurrences, where the rest on the contrary encodes MAKE basically as a two-place predicate. The other partly similar predicate, CONSIDER, is shown in Table 3. and has a different behaviour: it is used as a 3PP 8% (28) of the total occurencies (369), a very small amount if compared to MAKE, but still notable when compared to the synonym verb REGARD, which appears 120 times, always encoded as two-place predicate and never encoded as 3PP. Finally, CAUSE appears only 35 times correctly encoded as a two-place predicate with another eventive argument encoding the SC. The remaining cases are wrong two-place encoding. Types Adverb. Adject. Verbs Nouns Total VB(e1, x1, x4) 4 244 537 714 1499 80
Types Adverb. Adject. Verbs Nouns Total VB(e1, x1, e2) 0 5 2 28 35 VB(e1, x1) 0 8 1 0 9 VB(e1,x1, x2, x4) 0 0 0 4 4 Total 4 257 540 746 1547 Table 1.: Number of different types of eventive structures for CAUSE in LF entries Types Adverb. Adject. Verbs Nouns Total VB(e1, x1, x4) 5 193 846 1751 2795 VB(e1, x1, e2) 0 3 21 56 80 VB(e1, x1) 0 12 7 0 19 VB(e1,x1, x2, x4) 1 112 307 37 457 Total 6 320 1181 1844 3351 Table 2.: Number of different types of eventive structures for MAKE in LF entries Types Adverb. Adject. Verbs Nouns Total VB(e1, x1, x4) 0 14 52 270 336 VB(e1, x1, e2) 0 0 0 5 5 VB(e1, x1) 0 0 0 0 0 VB(e1,x1, x2, x4) 0 3 3 22 28 Total 0 17 55 297 369 Table 3.: Number of different types of eventive structures for CONSIDER in LF entries 4 Some general consideration on XWN Some general considerations over the whole dataset come from considering the amount of GOLD data with respect to NORMAL or SILVER, as shown in Table 4 below. Types Adverb. Adjectiv. Verbs Nouns Gold 3994 16059 14441 32844 Silver 0 4321 0 7228 Normal 0 0 0 54796 81
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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
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
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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
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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: (VP (VB patch) ) ) ) (VP (VBZ is) (
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
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Page 107 and 108: only the lexicon is fine-grained to
Page 109 and 110: Automatic Coreference Annotation in
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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
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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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Checkpoint Instances Google PT Our
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6 Conclusions In this paper we have
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An integrated web-based treebank an
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16] and we have earlier reported fr
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Figure 2: Screenshot of the interfa
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ple subcategorization frames may be
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4 Integrated interface for annotati
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of the 5th International Conference
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Translational divergences and their
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posed so far, Cyrus’ work did not
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allowed an appropriate coverage of
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damentales [...] 10 (the universal
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for all lex_pair(s,t) do if head an
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the treatment of typical translatio
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Building a treebank of noisy user-g
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Phenomenon Attested example Std. co
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Figure 1: French Social Media Bank
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Impact of treebank characteristics
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Det Adj N (a) Danish Det Adj N (b)
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sv: Bestämmelserna i detta avtal f
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100 90 80 Unlabelled attachment 70
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vidner , tilhørere og tiltalte wit
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mance is not inconsiderable, as was
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Genitives in Hindi Treebank: An Att
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A genitive can occur with complex p
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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