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The role of object novelty and pragmatic reasoning in referent selection and word learning (Study 2b) ...
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Disambiguation of Medical Abbreviations in French with Supervised Methods
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In: Studies in Health Technology and Informatics ; https://hal.archives-ouvertes.fr/hal-03335532 ; Studies in Health Technology and Informatics, 2021, ⟨10.3233/shti210171⟩ (2021)
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The role of object novelty and nameability in disambiguation and word learning ...
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Perceptual vowel contrast reduction in Australian English /l/-final rimes
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 9 ; 1868-6354 (2021)
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Italian Sense Inventory
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Poli, Francesca. - : Università di Pisa, 2021. : Istituto di Linguistica Computazionale “A. Zampolli” - Consiglio Nazionale delle Ricerche (ILC-CNR), 2021
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Not this, but that.' Exploring disambiguation in the context of multilingual word learning ...
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Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet
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In: Electronics; Volume 10; Issue 23; Pages: 2938 (2021)
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A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
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In: Information ; Volume 12 ; Issue 11 (2021)
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The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding
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In: ISPRS International Journal of Geo-Information ; Volume 10 ; Issue 9 (2021)
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Abstract:
Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed.
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Keyword:
deep learning; geocoding; named entity disambiguation; place name resolution
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URL: https://doi.org/10.3390/ijgi10090572
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Improving the Performance of Vietnamese&ndash ; Korean Neural Machine Translation with Contextual Embedding
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In: Applied Sciences ; Volume 11 ; Issue 23 (2021)
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NERWS: Towards Improving Information Retrieval of Digital Library Management System Using Named Entity Recognition and Word Sense
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In: Big Data and Cognitive Computing ; Volume 5 ; Issue 4 (2021)
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SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) ...
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SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) ...
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TERMS AND TEXTS: THE FUNCTIONALITY OF EDITORIAL-POLYGRAPHIC TERMS ...
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TERMS AND TEXTS: THE FUNCTIONALITY OF EDITORIAL-POLYGRAPHIC TERMS ...
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Not this, but that.' Exploring disambiguation in the context of multilingual word learning
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Semantically-oriented text planning for automatic summarization
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In: TDX (Tesis Doctorals en Xarxa) (2021)
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