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Description sémantique de" dans un premier temps" : de la composition syntagmatique au discours
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In: ISSN: 1963-1723 ; Discours - Revue de linguistique, psycholinguistique et informatique ; https://hal.archives-ouvertes.fr/hal-00808800 ; Discours - Revue de linguistique, psycholinguistique et informatique, Laboratoire LATTICE, 2011, 9, http://discours.revues.org/8563. ⟨10.4000/discours.8563⟩ (2011)
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Part-of relations, functionality and dependence
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In: The categorization of spatial entities in language and cognition ; https://hal.archives-ouvertes.fr/hal-01078739 ; Michel Aurnague; Maya Hickmann; Laure Vieu. The categorization of spatial entities in language and cognition, 20, John Benjamins Publishing Company, pp.307 - 336, 2007, Human Cognitive Processing, 978 90 272 2374 6. ⟨10.1075/hcp.20.18vie⟩ (2007)
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Reciprocals in Somali
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In: Reciprocals cross-linguistically ; https://halshs.archives-ouvertes.fr/halshs-00606972 ; Reciprocals cross-linguistically, Nov 2007, Berlin, Germany (2007)
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Extracting Meronymy Relationships from Domain- Specific, Textual Corporate Databases
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In: http://odur.let.rug.nl/%7Egosse/papers/Ashwin_Meronymy_NLDB.pdf
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Abstract:
Abstract. Various techniques for learning meronymy relationships from opendomain corpora exist. However, extracting meronymy relationships from domain-specific, textual corporate databases has been overlooked, despite numerous application opportunities particularly in domains like product development and/or customer service. These domains also pose new scientific challenges, such as the absence of elaborate knowledge resources, compromising the performance of supervised meronymy-learning algorithms. Furthermore, the domain-specific terminology of corporate texts makes it difficult to select appropriate seeds for minimally-supervised meronymylearning algorithms. To address these issues, we develop and present a principled approach to extract accurate meronymy relationships from textual databases of product development and/or customer service organizations by leveraging on reliable meronymy lexico-syntactic patterns harvested from an open-domain corpus. Evaluations on real-life corporate databases indicate that our technique extracts precise meronymy relationships that provide valuable operational insights on causes of product failures and customer dissatisfaction. Our results also reveal that the types of some of the domain-specific meronymy relationships, extracted from the corporate data, cannot be conclusively and unambiguously classified under well-known taxonomies of relationships.
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Keyword:
Meronymy; natural language processing; part-whole relations
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.5012
http://odur.let.rug.nl/%7Egosse/papers/Ashwin_Meronymy_NLDB.pdf
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