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

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G1 G2 G3 G4 G5 G6 G7 G1 x x x x x G2 x x G3 x x G4 x x G5 x x G6 x x x G7 x Table 1: Possible combinations son to the mention James Lewis Morrison, because although all the words of the first mention are included in the second mention, the second mention contains an intermediate word (Lewis). In order to decide in which groups to look for candidate mentions, we can use our knowledge of the composition of the groups. Thus, we know that coreferences between some groups are more likely than between others. For example, we can expect to find coreferences between the mentions of group 5 and group 6 because both groups include named entities, like the mentions Brad Pitt (G5) and the actor Brad Pitt (G6). By contrast, there will be no coreferences between the elements of the group 1 and group 5, for the simple reason that group 1 is created with no named entities and group 5 is created with named entities only. Consider the mention carpenter (G1) and the mention John Carpenter (G5). Although the word carpenter appears in both mentions, in the first one it refers to a profession and in the second to a surname. Therefore, we have discarded some combinations; the possible ones are summarized in Table 1. Once each mention is filed in its proper group and it is clear what combinations of groups we need to look for possible coreferences in, we can begin searching for possible coreferences for each mention. First, we search the mentions of each group for possible candidates. Consider a group with the mentions Juan Montero, Perez, Montero, Gallastegi, Buesa. The system would link the mentions Juan Montero and Montero. For some groups (G1 and G5), the system next tries to link mentions with mentions in other groups. For example, the mentions included in group 1 [the dog...] are the most general, so we match them with mentions from several groups (G2 [George’s dog...], G3 [the dog in Rome...], G4 [Nuria’s Madrid dog...], G6 [the dog Larry...]) due to the probability of finding coreferences in these morphologically compatible groups. However, it is also possible that two mentions in different groups could be coreferential only through a third mention. For example, we cannot directly join the mentions Michael Jordan and basketball player because we lack a clue that we could use to make the connection. But if we were to find the mention Michael Jordan the basketball player, we could use it to join all three mentions. 120
4.1.3 Coreference Selector Module The objective of this module is to return clusters of coreferences, validating or modifying the clusters that it receives from the previous module. The input of this module, then, is a set of clusters that links coreference candidates. In order to decide whether a cluster of coreferences is correct, the order in which the mentions of the cluster appear in the text is crucial. We can find the same mention in the text twice without it being coreferential. Consider this example: [Iñaki Perurena] has lifted a 325-kilo stone... [Perurena] has trained hard to get this record... His son [Jon Perurena] has his father’s strength... [Perurena] has lifted a 300-kilo cube-formed stone. In this example we find the mention Perurena twice. The previous module links these mentions, creating a cluster of four mentions [Iñaki Perurena, Perurena, Jon Perurena, Perurena]. However, Jon Perurena and Iñaki Perurena are not coreferential, so this cluster is not valid. To eliminate this type of erroneous linkage, the coreference selector module takes into account the order in which the mentions appear. In other words, it matches the mention Iñaki Perurena to the first Perurena mention and the mention Jon Perurena to the second Perurena mention, as this is most likely to have been the writer’s intention. The system labels all marked mentions as possible coreferents (m 1 , m 2 , m 3 , m 4 , m n ) and then proceeds through them one by one trying to find coreferences. The system uses the following procedure. (1) If there exists a mention (for example m 3 ) that agrees with an earlier mention (for example m 1 ) and there does not exist a mention between them (for example m 2 ) that is coreferential with the current mention (m 3 ) and not with the earlier mention (m 1 ), the module considers them (m 1 and m 3 ) coreferential. (2) If there exists a mention between the two mentions (for example m 2 ) that is coreferential with the current mention (m 3 ) but not with the earlier mention (m 1 ), the module will mark as coreferents the intermediate mention (m 2 ) and the current mention (m 3 ). Thus, the module forms, step by step, different clusters of coreferences, and the set of those strings forms the final result of the Nominal Coreference Resolution Process. Figure 1: Nominal coreference example. Figure 1 shows the result of the Nominal Coreference Resolution represented by the MMAX2 tool. The annotator then checks if the cluster of coreferences is correct. If it is, the coreference is annotated simply by clicking on it; if it is not, the annotator can easily correct it. Thus, the time employed in annotating coreferences is reduced. 121
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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
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: citizens of Bilbao] are very involv
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
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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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