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AIR-JPMC@SMM4H'22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling.

, , , , , , and . SMM4H@COLING, page 160-162. Association for Computational Linguistics, (2022)

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funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter., , , , and . SemEval@ACL, page 852-856. Association for Computational Linguistics, (2017)TipMaster: A Knowledge Base of Authoritative Local News Sources on Social Media., , , , and . AAAI, page 7825-7830. AAAI Press, (2018)Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches., , , , , and . IEEE BigData, page 4014-4016. IEEE Computer Society, (2016)Tweet Topic Classification Using Distributed Language Representations., , , , and . WI, page 81-88. IEEE Computer Society, (2016)Tweet Sentiment Analysis by Incorporating Sentiment-Specific Word Embedding and Weighted Text Features., , , , and . WI, page 568-571. IEEE Computer Society, (2016)CodeMirage: Hallucinations in Code Generated by Large Language Models., , , and . CoRR, (2024)Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank., , , , and . CIKM, page 2085-2088. ACM, (2016)TweetSift: Tweet Topic Classification Based on Entity Knowledge Base and Topic Enhanced Word Embedding., , , , and . CIKM, page 2429-2432. ACM, (2016)Witness Identification in Twitter., , , , and . SocialNLP@EMNLP, page 65-73. Association for Computational Linguistics, (2016)Discovering Relevant Hashtags for Health Concepts: A Case Study of Twitter., , , , and . AAAI Workshop: WWW and Population Health Intelligence, volume WS-16-15 of AAAI Technical Report, AAAI Press, (2016)978-1-57735-759-9.