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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Can pre-trained Transformers be used in detecting complex sensitive sentences? -- A Monsanto case study ... Timmer, Roelien C.; Liebowitz, David; Nepal, Surya. - : arXiv, 2022 BASE Show details 2 TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing Timmer, Roelien; Liebowitz, David; Nepal, Surya. - 2022 BASE Show details 3 The importance of social identity on password formulations [<Journal>] Grobler, Marthie [Verfasser]; Chamikara, M. A. P. [Verfasser]; Abbott, Jacob [Verfasser]. DNB Subject Category Language Show details 4 DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains ... Xu, Chang; Paris, Cecile; Nepal, Surya. - : arXiv, 2020 BASE Show details 5 Assessing Social License to Operate from the Public Discourse on Social Media ... The 28th International Conference on Computational Linguistics 2020; Nepal, Surya; Paris, Cecile. - : Underline Science Inc., 2020 BASE Show details 6 Recognising Agreement and Disagreement between Stances with Reason Comparing Networks ... Xu, Chang; Paris, Cecile; Nepal, Surya; Sparks, Ross. - : arXiv, 2019 Abstract: We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection. ... : To appear at the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1906.01392 https://dx.doi.org/10.48550/arxiv.1906.01392 BASE Hide details 7 Catering to your concerns: Automatic generation of personalised security-centric descriptions for Android apps Wu, Tingmin; Tang, Lihong; Zhang, Rongjunchen... In: ACM Transactions on Cyber-Physical Systems, Vol. 3, no. 4 (Sep 2019), article no. 36 (2019) BASE Show details 8 Cross-Target Stance Classification with Self-Attention Networks ... Xu, Chang; Paris, Cecile; Nepal, Surya. - : arXiv, 2018 BASE Show details 9 Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps ... Wu, Tingmin; Tang, Lihong; Zhang, Rongjunchen. - : arXiv, 2018 BASE Show details 10 Incorporating tweet relationships into topic derivation Nugroho, Robertus; Molla-Aliod, Diego; Yang, Jian. - : Singapore : Springer, 2016 BASE Show details

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