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

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appoint, elect, make, vote; certify, christen, declare, name, among others.”(ibid.97;107). Generalizing a small clause analysis in constituent structure is certainly useful, but we must also stress the fact that the theory underlying the construction of small clauses is not adequate for their treatment in LFs. Blurring the difference between the first pair of verbs I already introduced above, and the second pair, is harmful in many ways. In particular, in the case of of PUT and GIVE what we want to say is basically that, “there is a PUTting/GIVing event of the BOOK and the event is completed by the change_of_possession/change_of_position to a GOAL/RECIPIENT/LOCATION argument” The need to complete the meaning by means of the third argument is what makes the difference with the case of Optional Arguments. The same argument may be applied to verbs like CONSIDER CAUSE and SEE that we exemplify now with their LFs: -CONSIDER(e1,x1,x2,e2),John(x1),Mary(x2),silly(e2,x2) -CAUSE(e1,x1,x2,e2), John(x1), car(x2), stop(e2,x1,x2) In these LFs it is the secondary predication that completes the meaning with its own event structure. The secondary predication introduces a new event variable which is used to associate its meaning to the appropriate argument: it is Mary that is SILLY and not John; it is the car that STOPs and not John. Besides, both the CAR and MARY are real OBJect arguments of the main verb, which can be passivized: - Mary is considered silly (by many people). - The car was stopped (by a policeman). In other words, the Small Clause analysis treats the OBJect of the matrix clause as SUBJect of the SC in constituent structure, and this is basically wrong. What I am interested in, is checking the way in which these constituent structures have been represented in XWN and how the SC analysis has influenced the LF mapping. I will start by showing two examples of the way in which PUT is encoded, wrongly: the verb CUP, and the verb PATCH. put into a cup; "cup the milk" (S (S (VP (TO to) (VP (VB cup) ) ) ) (VP (VBZ is) (S (VP (TO to) (VP (VB put) (PP (IN into) (NP (DT a) (NN cup) ) ) ) ) ) ) cup:VB(e1, x1, x2) -> put:VB(e1, x1, x4) into:IN(e1, x3) cup:NN(x3) mend by putting a patch on; "patch a hole" (S (S (VP (TO to) 78
(VP (VB patch) ) ) ) (VP (VBZ is) (S (VP (TO to) (VP (VB mend) (PP (IN by) (S (VP (VBG putting) (NP (DT a) (NN patch) ) (PP (IN on) ) ) ) ) ) ) patch:VB(e1, x1, x2) -> mend:VB(e1, x1, x2) by:IN(e1, e3) put:VB(e3, x1, x5) patch:NN(x5) on:IN(e3, x2) In both cases, the verb PUT is encoded without the OBLique, which is then added as an ADJunct by means of the semantics of the preposition IN. In addition, the verb CUP does not tranfer the OBJect argument to the verb PUT which is left without arguments apart from the SUBJect x1; both x2 and x4 are treated as free variables. In the whole database, there are only three cases where the verb PUT is correctly encoded as a 3PP, and one of them is here below: make, make_up put in order or neaten; "make the bed"; "make up a room" make:VB(e1, x1, x2) -> put:VB(e2, x1, x3, x2) in_order:JJ(x3) or:CC(e1, e2, e3) neaten:VB(e3, x1, x2) caution, admonish, monish warn strongly; put on guard caution:VB(e1, x1, x2) -> put:VB(e1, x1, x3, x2) on_guard:JJ(x3) The fact that the third argument is computed as an ADJectival and not as a Prepositional ADJunct may explain its peculiar treatment, which is only found in similar contexts. In fact this treatment has also been used for MAKE when it is used with secondary predications as shown in the following two entries: darkened become or made dark darkened:JJ(x1) -> make:VB(e2, x5, x4, x1) dark:JJ(x4) intimidated made timid or fearful as by threats intimidated:JJ(x1) -> make:VB(e1, x6, x4, x1) timid:JJ(x4) fearful:JJ(x4) as_by:IN(e1, x2) threat:NN(x2) Here correctly, we see that MAKE is encoded as a 3PP and all variables as bound, apart from the SUBJect which is left implicit in all entries of this type. Finally I will look at CAUSE, which should be given a structure similar to FORCE, at least whenever an infinitival occurs as complement. Some examples here below: 79
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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
Page 21 and 22: the sequential order of nodes in a
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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
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Page 39 and 40: Example
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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
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Page 75 and 76: Types Adverb. Adject. Verbs Nouns T
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Page 79 and 80: DeepBank: A Dynamically Annotated T
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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
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Page 113 and 114: citizens of Bilbao] are very involv
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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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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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