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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.

, , , и . SemEval@ACL, стр. 191-197. Association for Computational Linguistics, (2017)

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Georg Man

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Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning., , и . IJCNN, стр. 1-8. IEEE, (2018)A comprehensive comparative study on term weighting schemes for text categorization with support vector machines., , , и . WWW (Special interest tracks and posters), стр. 1032-1033. ACM, (2005)Recognizing cross-lingual textual entailment with co-training using similarity and difference views., , , и . IJCNN, стр. 3705-3712. IEEE, (2014)Measuring short Text Semantic Similarity using multiple measurements., и . ICMLC, стр. 808-813. IEEE, (2013)Are Negative Samples Necessary in Entity Alignment?: An Approach with High Performance, Scalability and Robustness., , , и . CIKM, стр. 1263-1273. ACM, (2021)ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task., , и . SemEval@NAACL-HLT, стр. 1094-1098. Association for Computational Linguistics, (2018)ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures., , и . SemEval@NAACL-HLT, стр. 118-123. The Association for Computer Linguistics, (2013)Memory-Based Model with Multiple Attentions for Multi-turn Response Selection., , и . ICONIP (2), том 11302 из Lecture Notes in Computer Science, стр. 296-307. Springer, (2018)MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph., , , , и . WSDM, стр. 420-428. ACM, (2020)A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs., , , , , и . COLING, стр. 2075-2086. International Committee on Computational Linguistics, (2022)