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1. Semantic Pleonasm Detection

   https://doi.org/10.18653/v1/n18-2036  

   Kashefi, Omid ; Lucas, Andrew T. ; Hwa, Rebecca ( June 2018 , Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers))

   Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with inter-annotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods. 

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2. Semantic Network Interpretation

   https://doi.org/10.1109/WACVW54805.2022.00046  

   Guo, Pei ; Farrell, Ryan ( January 2022 , 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW))

   Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network’s decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network’s failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model’s performance and its distribution metrics like filter selectivity and concept sparseness. 

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3. Lexical Semantic Recognition

   https://doi.org/10.18653/v1/2021.mwe-1.6  

   Liu, Nelson F. ; Hershcovich, Daniel ; Kranzlein, Michael ; Schneider, Nathan ( January 2021 , Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021))
4. Semantic crash bucketing

   https://doi.org/10.1145/3238147.3238200  

   van Tonder, Rijnard ; Kotheimer, John ; Le Goues, Claire ( January 2018 , Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering)

   Precise crash triage is important for automated dynamic testing tools, like fuzzers. At scale, fuzzers produce millions of crashing inputs. Fuzzers use heuristics, like stack hashes, to cut down on duplicate bug reports. These heuristics are fast, but often imprecise: even after deduplication, hundreds of uniquely reported crashes can still correspond to the same bug. Remaining crashes must be inspected manually, incurring considerable effort. In this paper we present Semantic Crash Bucketing, a generic method for precise crash bucketing using program transformation. Semantic Crash Bucketing maps crashing inputs to unique bugs as a function of changing a program (i.e., a semantic delta). We observe that a real bug fix precisely identifies crashes belonging to the same bug. Our insight is to approximate real bug fixes with lightweight program transformation to obtain the same level of precision. Our approach uses (a) patch templates and (b) semantic feedback from the program to automatically generate and apply approximate fixes for general bug classes. Our evaluation shows that approximate fixes are competitive with using true fixes for crash bucketing, and significantly outperforms built-in deduplication techniques for three state of the art fuzzers. 

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5. Adversarial Semantic Collisions

   https://doi.org/10.18653/v1/2020.emnlp-main.344  

   Song, Congzheng ; Rush, Alexander ; Shmatikov, Vitaly ( January 2020 , EMNLP)

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