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LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets.

, , , and . SemEval@NAACL-HLT, page 673-678. The Association for Computer Linguistics, (2015)

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Combining Context Features by Canonical Belief Network for Chinese Part-Of-Speech Tagging., and . IJCNLP, page 907-912. The Association for Computer Linguistics, (2008)How about utilizing ordinal information from the distribution of unlabeled data., , , and . CIKM, page 289-298. ACM, (2010)Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese., , and . PACLIC, ACL, (2015)Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network., , , , , , and . Medical Image Anal., (2022)Minimizing energy consumption with reliability goal on heterogeneous embedded systems., , , and . J. Parallel Distrib. Comput., (2019)A Rule System for Chinese Time Entity Recognition by Comprehensive Linguistic Study., and . IJCNLP, page 795-801. Asian Federation of Natural Language Processing / ACL, (2013)Corpus-based Study and Identification of Mandarin Chinese Light Verb Variations., , , and . VarDial@COLING, page 1-10. Association for Computational Linguistics and Dublin City University, (2014)Annotate and Identify Modalities, Speech Acts and Finer-Grained Event Types in Chinese Text., and . LG-LP@COLING, page 157-166. Association for Computational Linguistics and Dublin City University, (2014)Unsupervised Morphology Learning with Statistical Paradigms., , , and . COLING, page 44-54. Association for Computational Linguistics, (2018)LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets., , , and . SemEval@NAACL-HLT, page 673-678. The Association for Computer Linguistics, (2015)