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Informationsextraktion aus Wirtschaftsnachrichten über Unternehmenszusammenschlüsse mit lokalen Grammatiken
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IDS Mannheim
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Learning translations via images with a massively multilingual image dataset
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Losing Shahrazad: A Distant Reading of 1001 Nights
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In: Senior Projects Spring 2018 (2018)
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Bangime: Secret Language, Language Isolate, or Language Island?
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In: https://hal.archives-ouvertes.fr/hal-01867003 ; 2018 (2018)
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A database of German definitory contexts from selected web sources
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In: 11th International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.archives-ouvertes.fr/hal-01798704 ; 11th International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan. pp.3068-3073 (2018)
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Deep interactive text prediction and quality estimation in translation interfaces
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In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
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BASE
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Machine translation evaluation resources and methods: a survey
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In: Han, Lifeng orcid:0000-0002-3221-2185 (2018) Machine translation evaluation resources and methods: a survey. In: IPRC- Irish Postgraduate Research Conference, 8-9 Nov 2018, Dublin, Ireland. (2018)
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BASE
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Semantic reranking of CRF label sequences for verbal multiword expression identification
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In: Moreau, Erwan, Alsulaimani, Ashjan, Maldonado, Alfredo orcid:0000-0001-8426-5249 , Han, Lifeng, Vogel, Carl orcid:0000-0001-8928-8546 and Dutta Chowdhury, Koel (2018) Semantic reranking of CRF label sequences for verbal multiword expression identification. In: Markantonatou, Stella, Ramisch, Carlos orcid:0000-0001-7466-9039 , Savary, Agata and Vincze, Veronika orcid:0000-0002-9844-2194 , (eds.) Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop. Language Science Press, Berlin, pp. 177-207. ISBN 978-3-96110-124-5 (2018)
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Abstract:
Verbal multiword Expressions (VMWE) identification can be addressed successfully as a sequence labeling problem via conditional random fields (CRFs) by returning the one label sequence with maximal probability. This work describes a system that reranks the top 10 most likely CRF candidate VMWE sequences using a decision tree regression model. The reranker aims to operationalize the intuition that a non-compositional MWE can have a different distributional behavior than that of its constituent words. This is why it uses semantic features based on comparing the context vector of a candidate expression against those of its constituent words. However, not all VMWE are non-compositional, and analysis shows that non-semantic features also play an important role in the behavior of the reranker. In fact, the analysis shows that the combination of the sequential approach of the CRF component with the context-based approach of the reranker is the main factor of improvement: our reranker achieves a 12% macro-average F1-score improvement on the basic CRF method, as measured using data from PARSEME shared task on VMWE identification.
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Keyword:
Algorithms; Artificial intelligence; Computational linguistics; Computer engineering; conditional random fields; Language; Linguistics; Machine learning; Multiword Expression; MWE identification; semantic reranking
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URL: http://doras.dcu.ie/24500/
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Unlocking cultural conceptualisation in indigenous language resources: collaborative computing methodologies
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In: Dorn, Amelie, Wandl-Vogt, Eveline orcid:0000-0002-0802-0255 , Abgaz, Yalemisew orcid:0000-0002-3887-5342 , Benito Santos, Alejandro orcid:0000-0001-5317-6390 and Therón, Roberto orcid:0000-0001-6739-8875 (2018) Unlocking cultural conceptualisation in indigenous language resources: collaborative computing methodologies. In: 11th Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki, Japan. ISBN 979-10-95546-22-1 (2018)
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