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Stance Detection with Bidirectional Conditional Encoding
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has assumed that eitherExpand
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SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks andExpand
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LODifier: Generating Linked Data from Unstructured Text
The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extractedExpand
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Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge ofExpand
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A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towardsExpand
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Latent Multi-Task Architecture Learning
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possibleExpand
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Sluice networks: Learning what to share between loosely related tasks
Multi-task learning is motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from relatedExpand
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emoji2vec: Learning Emoji Representations from their Description
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available,Expand
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Discourse-aware rumour stance classification in social media using sequential classifiers
Abstract Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming ofExpand
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Generalisation in named entity recognition: A quantitative analysis
Quantitative study of NER performance in diverse corpora of different genres, including newswire and social media.Multiple state of the art NER approaches are tested.Possible reasons for NER failureExpand
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