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ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity.

, , , and . SemEval@ACL, page 191-197. Association for Computational Linguistics, (2017)

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Recognizing cross-lingual textual entailment with co-training using similarity and difference views., , , and . IJCNN, page 3705-3712. IEEE, (2014)Measuring short Text Semantic Similarity using multiple measurements., and . ICMLC, page 808-813. IEEE, (2013)ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures., , and . SemEval@NAACL-HLT, page 118-123. The Association for Computer Linguistics, (2013)ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task., , and . SemEval@NAACL-HLT, page 1094-1098. Association for Computational Linguistics, (2018)A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs., , , , , and . COLING, page 2075-2086. International Committee on Computational Linguistics, (2022)Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning., , and . IJCNN, page 1-8. IEEE, (2018)A comprehensive comparative study on term weighting schemes for text categorization with support vector machines., , , and . WWW (Special interest tracks and posters), page 1032-1033. ACM, (2005)Are Negative Samples Necessary in Entity Alignment?: An Approach with High Performance, Scalability and Robustness., , , and . CIKM, page 1263-1273. ACM, (2021)Memory-Based Model with Multiple Attentions for Multi-turn Response Selection., , and . ICONIP (2), volume 11302 of Lecture Notes in Computer Science, page 296-307. Springer, (2018)Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition., , and . ACL (1), page 476-485. The Association for Computer Linguistics, (2013)