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Finding Experiential Stress in Tweets by Utilizing both Explicit and Implicit Stress Data., and . IEEE BigData, page 5916-5918. IEEE, (2021)A Discriminative Learning Model for Coordinate Conjunctions., and . EMNLP-CoNLL, page 610-619. ACL, (2007)Target Evaluation for Neural Language Model using Japanese Case Frame., , and . KDIR, page 251-258. SCITEPRESS, (2020)Coordinate Structure Analysis with Global Structural Constraints and Alignment-Based Local Features., , , and . ACL/IJCNLP, page 967-975. The Association for Computer Linguistics, (2009)Reducing Hubness for Kernel Regression., , , , and . SISAP, volume 9371 of Lecture Notes in Computer Science, page 339-344. Springer, (2015)Reducing Hubness: A Cause of Vulnerability in Recommender Systems., , , and . SIGIR, page 815-818. ACM, (2015)Annotating Cohesive Statements of Anatomical Knowledge Toward Semi-automated Information Extraction., , , and . KDIR, page 342-347. SciTePress, (2014)Localized Centering: Reducing Hubness in Large-Sample Data., , , , , and . AAAI, page 2645-2651. AAAI Press, (2015)Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness., , , , and . AAAI, page 1659-1665. AAAI Press, (2016)Coordination-Aware Dependency Parsing (Preliminary Report)., , , and . IWPT, page 66-70. Association for Computational Linguistics, (2015)