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Mining the Web for Collocations: IR Models of Term Associations., , , , , , and . CICLing (1), volume 9623 of Lecture Notes in Computer Science, page 177-194. Springer, (2016)Sixteen Years of Phishing User Studies: What Have We Learned?, and . CoRR, (2021)Identifying Reference Spans: Topic Modeling and Word Embeddings help IR., , , and . CoRR, (2017)PhishBench 2.0: A Versatile and Extendable Benchmarking Framework for Phishing., , , and . CCS, page 2077-2079. ACM, (2020)Scaling and Effectiveness of Email Masquerade Attacks: Exploiting Natural Language Generation., , , and . AsiaCCS, page 469-482. ACM, (2017)An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs., , , and . IEEE Access, (2020)Diverse Datasets and a Customizable Benchmarking Framework for Phishing., , , , , and . IWSPA@CODASPY, page 35-41. ACM, (2020)Identifying reference spans: topic modeling and word embeddings help IR., , , and . Int. J. Digit. Libr., 19 (2-3): 191-202 (2018)University of Houston at CL-SciSumm 2016: SVMs with tree kernels and Sentence Similarity., , , and . BIRNDL@JCDL, volume 1610 of CEUR Workshop Proceedings, page 113-121. CEUR-WS.org, (2016)ICE: Idiom and Collocation Extractor for Research and Education., , , and . EACL (Software Demonstrations), page 108-111. Association for Computational Linguistics, (2017)