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One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method to address this limitation. We outline a novel approach for taxonomy learning that (1) defines concepts as synsets, (2) learns density-based approximations of contextualized word representations, and (3) can measure similarity and hypernymy among them.", "ee" : "http://arxiv.org/abs/1902.02169", "language" : "English", "bibtexKey": "journals/corr/abs-1902-02169" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b05149be0c2e6b80ac4e4d2ab3dfd193/lschmelzeisen", "tags" : [ "@AnalyticComp","AnalyticComputing","dblp","myown" ], "intraHash" : "b05149be0c2e6b80ac4e4d2ab3dfd193", "interHash" : "8ecf60543c716db98189eb98c772be4f", "label" : "CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.", "user" : "lschmelzeisen", "description" : "", "date" : "2020-11-26 10:05:00", "changeDate" : "2020-11-26 09:24:09", "count" : 6, "pub-type": "article", "journal": "CoRR", "year": "2019", "url": "https://arxiv.org/abs/1904.03084", "author": [ "Ipek Baris","Lukas Schmelzeisen","Steffen Staab" ], "authors": [ {"first" : "Ipek", "last" : "Baris"}, {"first" : "Lukas", "last" : "Schmelzeisen"}, {"first" : "Steffen", "last" : "Staab"} ], "volume": "abs/1904.03084","abstract": "This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. 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For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). 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