GWC 2008

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8 Javier Álvez et al. that WordNet simply is not the kind of taxonomy required, fact which can be due to several reasons: incompleteness, incorrect structuring, or perhaps that its structuring should be arranged differently for a particular NLP task. Bearing these drawbacks in mind, some authors have turned to use the ontologies mapped onto WordNet to determine new sets of classes. For instance, [20] and [21] have already used the MCR including SUMO, DO, WN16 Semantic (Lexicographer’s) Files and a preliminary rough expansion of the TCO for Word Sense Disambiguation. For many tasks, it seems that using a feature-annotated lexicon seems more appropriate than using the WordNet tree-structure, since (i) the WordNet hierarchy is not consistently structured [22] and (ii) a feature-annotated lexicon allows to make predictions based on measures of similarity even for words that, being sparsely distributed in WordNet, can only be generalized by reaching common hypernyms in levels too high in the hierarchy. Besides, a multiple-feature design allows to naturally depict semantically complex concepts, such as so-called dot-objects [6], e.g., intrinsically polysemic words such as “letter”, since a letter is something that can both be destroyed and carry information (as in “I burnt your love letter”). These aspects of meaning can be easily coded through using the EWN TCO, as shown in (1) (1) “letter”: FUNCTION: LanguageRepresentation FORM: Object In this direction, [23] uses a lexicon augmented with EWN TCO features both to implement selectional restrictions to limit the search space when parsing and to perform type-coercion in a dialogue system. 3 Methodology Our methodology for annotating the ILI with the TCO 3 is based on the common assumption that hyponymy corresponds to feature set inclusion [24, p.8] and in the observation that, since WordNets are taken to be crucially structured by hyponymy “ (…) by augmenting important hierarchy nodes with basic semantic features, it is possible to create a rich semantic lexicon in a consistent and cost-effective way after inheriting these features through the hyponymy relations" [9, pp. 204-205]. Nevertheless, performing such operation is not straightforward, as WordNets are not consistently structured by hyponymy [22]. Moreover, WordNets allow multiple inheritance. These are both drawbacks to overcome and situations to take advantage of by our methodology. As told above, within the EWN project, a limited set of lexical base concepts 4 (the BCs) was annotated with TCO features. Despite being 3 We use WN16 since the ILI is drawn up on this version of WordNet 4 Base Concepts (BCs) should not be confused with Basic Level Concepts (BLCs) as defined by [26] but in a future work BCs can be taken as a starting set to define that of BLCs. Since BLCs are supposed to be richer in distinctive features and the most psychologically salient lexical categories, they can also be relevant for advanced NLP tasks.
Consistent Annotation of EuroWordNet with the Top Concept Ontology 9 largely general in meaning, this set did not cover all of the upper level nodes in the WordNets. This was clearly a drawback for expanding features down all of WN1.6, thus the first step of our work consisted of annotating the gaps up the hierarchy, from the BCs to the Unique beginners. This was made semiautomatically: given that every synset in WN1.6 originally belongs to a so-called Semantic File (a flat list of 45 lexicographer files), those synsets were assigned a TCO feature via a table of expected equivalence between TCO nodes and Semantic Files. This made the WN1.6 ready to be fully populated with at least one feature per synset. Nevertheless, in many cases, synsets got more than one feature, for one or more of the following reasons: - They are BCs, so they were manually annotated with more than one feature - In addition to their own manual annotation, they inherit features from one or more hypernyms - They inherit features from different hypernyms, either located at different levels in a single line of hierarchy or by the effect of multiple inheritance An initial rough expansion was the first ground for revision and inspection, following the strategy defined in [25]. The a task has lasted for about three years and has involved several re-expansion cycles. The manual work has been based on TCO feature incompatibilities. It consisted in automatically detecting co-occurrences in a synset of pairs of incompatible features. The axiomatic incompatibilities are the following: - 1stOrderEntity - 2ndOrderEntity - 1stOrderEntity - 3rdOrderEntity - 3rdOrderEntity - 2ndOrderEntity [except for SituationComponent] - 3rdOrderEntity - Mental 5 - Object - Substance - Gas - Liquid - Solid - Artifact - Natural - Animal - Creature - Human - Plant - Dynamic - Static - BoundedEvent - UnboundedEvent - Property - Relation - Physical - Mental - Agentive - Phenomenal – Stimulating The first rough expansion described above caused the following number of feature conflicts: 5 The incompatibility between 3rdOrderEntity and Mental and the compatibility between 3rdOrderEntity and Situation Components is explained below.
Page 1 and 2: A. Tanács, D. Csendes, V. Vincze,
Page 3 and 4: Preface We are very pleased to hold
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Page 7 and 8: vii Methods and Results of the Hung
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Page 52 and 53: Some Issues in the Construction of
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58 Eduard Barbu and Massimo Poesio
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Enhancing WordNets with Morphologic
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76 Sonja Bosch, Christiane Fellbaum
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92 Cristina Butnariu and Tony Veale
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100 Cristina Butnariu and Tony Veal
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102 Thatsanee Charoenporn et al. la
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108 Thatsanee Charoenporn et al. Ta
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110 Thatsanee Charoenporn et al. 6.
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112 Peter Clark, Christiane Fellbau
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An Evaluation Procedure for Word Ne
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122 Irene Cramer and Marc Finthamme
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148 Gerard de Melo and Gerhard Weik
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Words, Concepts and Relations in th
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164 Magdalena Derwojedowa et al. Wo
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166 Magdalena Derwojedowa et al. ku
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168 Magdalena Derwojedowa et al. ev
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170 Magdalena Derwojedowa et al. Fi
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172 Magdalena Derwojedowa et al. 3.
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174 Magdalena Derwojedowa et al. of
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176 Magdalena Derwojedowa et al. Re
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Exploring and Navigating: Tools for
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180 Marc Finthammer and Irene Crame
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186 Darja Fišer 2 Methodology 2.1
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188 Darja Fišer (1) a Det-Noun phr
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190 Darja Fišer 4 Evaluation 4.1 S
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192 Darja Fišer Acknowledgements I
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The Global WordNet Grid Software De
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196 Aleš Horák, Karel Pala, and A
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198 Aleš Horák, Karel Pala, and A
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The Development of a Complex-Struct
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202 Aleš Horák, Piek Vossen, and
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210 Chu-Ren Huang, Chiyo Hotani, Tz
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222 Chu-Ren Huang, Pei-Yi Hsiao, I-
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230 Kadri Kerner The modal verbs ar
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232 Kadri Kerner Sometimes the near
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234 Kadri Kerner Some researchers [
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236 Kadri Kerner Ettevõtmine Toime
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238 Kadri Kerner Conference on Lang
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240 Svetla Koeva, Cvetana Krstev, a
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Language Independent and Language D
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256 Judit Kuti, Károly Varasdi, Á
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270 Jurie le Roux, Koliswa Moropa,
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282 Harald Lüngen, Claudia Kunze,
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298 Niloofar Mansoory and Mahmood B
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Towards a Morphodynamic WordNet of
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306 Nazaire Mbame 3.1 The semantic
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308 Nazaire Mbame from the proto mi
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310 Nazaire Mbame 4 Conclusion As s
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312 Márton Miháltz et al. word se
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314 Márton Miháltz et al. to the
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316 Márton Miháltz et al. 2.3 Adv
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318 Márton Miháltz et al. 3.2 Eva
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320 Márton Miháltz et al. and sem
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322 Rajat Kumar Mohanty et al. 1 In
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324 Rajat Kumar Mohanty et al. Tabl
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330 Rajat Kumar Mohanty et al. Fig.
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332 Rajat Kumar Mohanty et al. Once
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Estonian WordNet: Nowadays Heili Or
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336 Heili Orav, Kadri Vider, Neeme
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338 Heili Orav, Kadri Vider, Neeme
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340 Bolette Sandford Pedersen and S
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350 Ida Raffaelli, Marko Tadić, Bo
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376 Nils Reiter and Paul Buitelaar
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388 Horacio Rodríguez et al. synse
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392 Horacio Rodríguez et al. they
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394 Horacio Rodríguez et al. 3.2.2
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396 Horacio Rodríguez et al. Table
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400 Horacio Rodríguez et al. ‏:
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402 Horacio Rodríguez et al. Table
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Building a WordNet for Persian Verb
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408 Masoud Rouhizadeh, Mehrnoush Sh
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414 Mehrnoush Shamsfard semantic re
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418 Mehrnoush Shamsfard FarsNet for
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420 Virach Sornlertlamvanich et al.
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Extraction of Selectional Preferenc
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538 Hamza Zidoum The lexicographer
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544 Hamza Zidoum 26. Modern Standar
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546 Moropa, K. 269 Neri, F. 474 Nim
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