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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 Hits 1 – 6 of 6 1 Deep Learning for Text Style Transfer: A Survey ... Jin, Di; Jin, Zhijing; Hu, Zhiting. - : ETH Zurich, 2022 BASE Show details 2 Deep Learning for Text Style Transfer: A Survey Vechtomova, Olga; Jin, Zhijing; Hu, Zhiting... In: Computational Linguistics, 48 (1) (2022) BASE Show details 3 Deep Learning for Text Style Transfer: A Survey ... Jin, Di; Jin, Zhijing; Hu, Zhiting. - : arXiv, 2020 BASE Show details 4 Related Entity Finding: University of Waterloo at TREC 2010 Entity Track Vechtomova, Olga In: DTIC (2010) Abstract: The University of Waterloo participated in the Related Entity Finding task of the Entity track. Our goal is to investigate whether related entity finding problem can be addressed by unsupervised approaches that rely primarily on statistical methods and common linguistic tools, such as named-entity taggers and syntactic parsers. We approach the related entity finding problem by first retrieving documents in response to the query, and extracting an initial set of candidate entities from the text of the documents. As a separate step, we automatically construct a set of seed entities, which represent hyponyms of the target entity category specified in the narrative, and then rank the candidate entities by their similarity to the seeds. An example of the target entity category name is "authors", extracted from the narrative "Authors awarded an Anthony Award at Bouchercon in 2007" (2009 topic #14). The system extracts category names from the free-text narrative, finds seed entities belonging to each category, and computes the similarity of candidate entities to the seeds. ; Presented at the Text REtrieval Conference (TREC 2010) (19th) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text Retrieval Conference (TREC 2010) (19th), 2010. Sponsored in part by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA). Keyword: *INFORMATION RETRIEVAL; CANADA; ENTITIES; EXTRACTION; FOREIGN REPORTS; Information Science; LINGUISTICS; RANKING; RELATED ENTITY FINDING; STATISTICAL PROCESSES; SYMPOSIA URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA546752 http://www.dtic.mil/docs/citations/ADA546752 BASE Hide details 5 University of Waterloo at TREC 2008 Blog Track Vechtomova, Olga In: DTIC (2008) BASE Show details 6 Noun phrases in interactive query expansion and document ranking Vechtomova, Olga In: Information Retrieval Journal. - Dordrecht [u.a.] : Springer Science + Business Media B.V. 9 (2006) 4, 399-420 BLLDB Show details

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