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You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP.

, , , and . EMNLP/IJCNLP (1), page 4706-4716. Association for Computational Linguistics, (2019)

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Beyond linear chain: a journey through conditional random fields for information extraction from text.. SIGIR Forum, 48 (1): 44 (2014)A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining., , , , , and . EMNLP, page 2533-2538. The Association for Computational Linguistics, (2015)On the impact of entity linking in microblog real-time filtering., , , and . SAC, page 1066-1071. ACM, (2015)You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP., , , and . EMNLP/IJCNLP (1), page 4706-4716. Association for Computational Linguistics, (2019)ISTI@SemEval-2 Task 8: Boosting-Based Multiway Relation Classification., , and . SemEval@ACL, page 218-221. The Association for Computer Linguistics, (2010)Sentence-Based Active Learning Strategies for Information Extraction., , and . IIR, volume 560 of CEUR Workshop Proceedings, page 41-45. CEUR-WS.org, (2010)Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining., , , and . ECIR, volume 8416 of Lecture Notes in Computer Science, page 273-285. Springer, (2014)An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences., and . EMNLP, page 898-906. ACL, (2014)ISTI@TREC Microblog Track 2011: Exploring the Use of Hashtag Segmentation and Text Quality Ranking., , , and . TREC, Special Publication 500-296, National Institute of Standards and Technology (NIST), (2011)The Impacts of Low-Quality Training Data on Information Extraction from Clinical Reports., and . ERCIM News, (2018)