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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 47 1 Opening Ceremony ... The 28th International Conference on Computational Linguistics 2020; Scott, Donia. - : Underline Science Inc., 2020 BASE Show details 2 IntKB: A Verifiable Interactive Framework for Knowledge Base Completion Kunpeng, Guo; Feuerriegel, Stefan; Diefenbach, Dennis... In: Proceedings of the 28th International Conference on Computational Linguistics (2020) BASE Show details 3 A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples Wattenhofer, Roger; Meng, Zhao In: Proceedings of the 28th International Conference on Computational Linguistics (2020) BASE Show details 4 Morphologically Aware Word-Level Translation Czarnowska, Paula; Copestake, Ann; Ruder, Sebastian... In: Proceedings of the 28th International Conference on Computational Linguistics (2020) BASE Show details 5 Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction. Mandya, Angrosh; Coenen, Frans; Bollegala, Danushka. - : International Committee on Computational Linguistics, 2020 BASE Show details 6 Autoencoding Improves Pre-trained Word Embeddings. Kaneko, Masahiro; Bollegala, Danushka. - : International Committee on Computational Linguistics, 2020 BASE Show details 7 Specializing unsupervised pretraining models for word-level semantic similarity Ponti, Edoardo Maria; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, ACL, 2020 BASE Show details 8 XHate-999: analyzing and detecting abusive language across domains and languages Glavaš, Goran; Karan, Mladen; Vulić, Ivan. - : Association for Computational Linguistics, 2020 BASE Show details 9 Parsers know best: German PP attachment revisited Do, Bich-Ngoc; Rehbein, Ines. - : ACM Digital Library, 2020 BASE Show details 10 Towards instance-level parser selection for cross-lingual transfer of dependency parsers Litschko, Robert; Vulić, Ivan; Agić, Želiko. - : Association for Computational Linguistics, 2020 BASE Show details 11 KBGen - Text Generation for Knowledge Bases as a New Shared Task Banik, Eva; Gardent, Claire; Scott, Donia... In: Proceedings of the seventh International Natural Language Generation Conference ; The seventh International Natural Language Generation Conference. Starved Rock, Illinois, USA. ; https://hal.archives-ouvertes.fr/hal-00768616 ; The seventh International Natural Language Generation Conference. Starved Rock, Illinois, USA., May 2012, Starved Rock, Illinois, United States. pp.141-146 (2012) BASE Show details 12 Corpus annotation as a scientific task Scott, Donia; Barone, Rossano; Koeling, Rob. - 2012 BASE Show details 13 A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus Zhou, Deyu; He, Yulan. - : Association for Computational Linguistics, 2008 Abstract: We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. URL: https://publications.aston.ac.uk/id/eprint/18302/1/Hybrid_generative_discriminative_framework_to_train_a_semantic.pdf https://publications.aston.ac.uk/id/eprint/18302/ BASE Hide details 14 Special section on question answering in restricted domains Vicedo, José Luis; Scott, Donia; Power, Richard... In: Computational linguistics. - Cambridge, Mass. : MIT Press 33 (2007) 1, 41-133 BLLDB OLC Linguistik Show details 15 Composing Questions through Conceptual Authoring Hallett, Catalina; Power, Richard; Scott, Donia In: Computational linguistics. - Cambridge, Mass. : MIT Press 33 (2007) 1, 105 OLC Linguistik Show details 16 A reference architecture for natural language generation systems Mellish, Chris; Scott, Donia; Cahill, Lynne J.... In: Natural language engineering. - Cambridge : Cambridge University Press 12 (2006) 1, 1-34 BLLDB Show details 17 Computational approaches to discourse and document processing Péry-Woodley, Marie-Paule; Scott, Donia In: Traitement automatique des langues. - Paris : ATALA 47 (2006) 2, 7-19 BLLDB Show details 18 Visualising discourse coherence in non-linear documents Mancini, Clara; Scott, Donia; Shum, Simon J. Buckingham In: Traitement automatique des langues. - Paris : ATALA 47 (2006) 2, 137-168 BLLDB Show details 19 Generating multimedia presentations: from plain text to screenplay Piwek, Paul; Power, Richard; Scott, Donia. - : Springer, 2005 BASE Show details 20 Software architecture for language engineering Cunningham, Hamish (Hrsg.); Scott, Donia (Hrsg.) In: Natural language engineering. - Cambridge : Cambridge University Press 10 (2004) 3-4, 205-392 BLLDB Show details Page: 1 2 3

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