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Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches.

, , , , , and . IEEE BigData, page 4014-4016. IEEE Computer Society, (2016)

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SPread: Automated Financial Metric Extraction and Spreading Tool from Earnings Reports., , and . WSDM, page 853-856. ACM, (2020)Data Sets: Word Embeddings Learned from Tweets and General Data., , , and . CoRR, (2017)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)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)Using counterfactual contrast to improve compositional generalization for multi-step quantitative reasoning., , and . ACL (1), page 14930-14943. Association for Computational Linguistics, (2023)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)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)