Komputationell semantik

Diarienummer GU 2026/4229

Litteraturlista LT2213 & LT2813 VT2027

- Lasha Abzianidze. 2017. Langpro: Natural language theorem prover. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 115–120, Copenhagen, Denmark. Association for Computational Linguistics.

- Marco Baroni, Georgiana Dinu, and Germán Kruszewski. 2014. Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pages 238–247, Baltimore, Maryland. Association for Computational Linguistics.

- Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 610–623, New York, NY. ACM.

- Emily M. Bender and Alexander Koller. 2020. Climbing towards nlu: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5185–5198. Association for Computational Linguistics.

- Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137–1155.

- Steven Bird, Ewan Klein, and Edward Loper. 2009. Analyzing the meaning of sentences. In Natural Language Processing with Python, chapter 10. O’Reilly Media.

- Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.

- Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632–642, Lisbon, Portugal. Association for Computational Linguistics.

- Samuel R. Bowman and Xiaodan Zhu. 2019. Deep learning for natural language inference. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pages 6–8, Minneapolis, Minnesota. Association for Computational Linguistics.

- Stephen Clark. 2015. Vector space models of lexical meaning. In The Handbook of Contemporary Semantic Theory, 2nd edition, chapter 16, pages 493–522. Wiley-Blackwell.

- Robin Cooper, Dick Crouch, Jan van Eijck, Chris Fox, Johan van Genabith, Jan Jaspars, Hans Kamp, David Milward, Manfred Pinkal, Massimo Poesio, and Steve Pulman. 1996. Using the framework. Technical report LRE 62-051 D-16, The FraCaS Consortium.

- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.

- Simon Dobnik, Robin Cooper, Adam Ek, Bill Noble, Staffan Larsson, Nikolai Ilinykh, Vladislav Maraev, and Vidya Somashekarappa. 2022. In search of meaning and its representations for computational linguistics. In Proceedings of the 2022 CLASP Conference on (Dis)embodiment, pages 30–44, Gothenburg, Sweden. Association for Computational Linguistics.

- Alex Duchnowski, Ellie Pavlick, and Alexander Koller. 2025. Ehop: A dataset of everyday np-hard optimization problems. arXiv:2502.13776.

- Katrin Erk. 2012. Vector space models of word meaning and phrase meaning: A survey. Language and Linguistics Compass, 6(10):635–653.

- Mehdi Ghanimifard and Simon Dobnik. 2017. Learning to compose spatial relations with grounded neural language models. In Proceedings of the 12th International Conference on Computational Semantics (IWCS), Long Papers. Association for Computational Linguistics.

- Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2: Short Papers, pages 107–112, New Orleans, Louisiana. Association for Computational Linguistics.

- Cameron R. Jones, Benjamin Bergen, and Sean Trott. 2024. Do multimodal large language models and humans ground language similarly? Computational Linguistics, 50(4):1415–1440.

- Pablo Contreras Kallens and Morten H. Christiansen. 2025. Distributional semantics: Meaning through culture and interaction. Topics in Cognitive Science, 17(3):739–769.

- Aikaterini-Lida Kalouli, Hai Hu, Alexander F. Webb, Lawrence S. Moss, and Valeria de Paiva. 2023. Curing the sick and other nli maladies. Computational Linguistics, 49(1):199–243.

- Najoung Kim and Tal Linzen. 2020. Cogs: A compositional generalization challenge based on semantic interpretation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 9087–9105. Association for Computational Linguistics.

- Mikael Kågebäck and Hans Salomonsson. 2016. Word sense disambiguation using a bidirectional lstm. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 51–56, Osaka, Japan. COLING 2016 Organizing Committee.

- Brenden M. Lake and Marco Baroni. 2018. Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. In Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 2873–2882. PMLR.

- Alessandro Lenci. 2018. Distributional models of word meaning. Annual Review of Linguistics, 4:151–171.

- Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Advances in Neural Information Processing Systems 27, pages 2177–2185.

- Bingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen, Yuekun Yao, and Najoung Kim. 2023. Slog: A structural generalization benchmark for semantic parsing. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics

- Bill MacCartney and Christopher D. Manning. 2007. Natural logic for textual inference. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pages 193–200, Prague. Association for Computational Linguistics.

- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781.

- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc.

- Filip Miletić and Sabine Schulte im Walde. 2024. Semantics of multiword expressions in transformer-based models: A survey. Transactions of the Association for Computational Linguistics, 12:593–612.

- Jeff Mitchell and Mirella Lapata. 2008. Vector-based models of semantic composition. In Proceedings of ACL-08: HLT, pages 236–244, Columbus, Ohio. Association for Computational Linguistics.

- Jeff Mitchell and Mirella Lapata. 2010. Composition in distributional models of semantics. Cognitive Science, 34(8):1388–1429.

- Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Adversarial nli: A new benchmark for natural language understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.

- Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, and Chitta Baral. 2024. Logicbench: Towards systematic evaluation of logical reasoning ability of large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pages 13679–13707, Bangkok, Thailand. Association for Computational Linguistics.

- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, pages 2227–2237, New Orleans, Louisiana. Association for Computational Linguistics.

- Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. Technical report, OpenAI.

- Matthew Stone. 2016. Semantics and computation. In The Cambridge Handbook of Formal Semantics, pages 775–800. Cambridge University Press.

- Aarne Talman, Anssi Yli-Jyrä, and Jörg Tiedemann. 2019. Sentence embeddings in nli with iterative refinement encoders. Natural Language Engineering, 25(4):467–482.

- Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019. Bert rediscovers the classical nlp pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4593–4601, Florence, Italy. Association for Computational Linguistics.

- Peter D. Turney and Patrick Pantel. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37:141–188.

- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30, pages 5998–6008. Curran Associates, Inc.

- Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. Superglue: A stickier benchmark for general-purpose language understanding systems. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc.

- Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353–355, Brussels, Belgium. Association for Computational Linguistics.

- Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.

Ca 900 pages.