% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@article{buell2023dimabsa,
  author = {Lung-Hao Lee and Liang-Chih Yu and Natalia Loukachevitch and Ilyas Alimova and Alexander Panchenko and Tzu-Mi Lin and Zhe-Yu Xu and Jian-Yu Zhou and Guangmin Zheng and Jin Wang and Sharanya Awasthi and Jonas Becker and Jan Philip Wahle and Terry Ruas and Shamsuddeen Hassan Muhammad and Saif M. Mohammad},
  title = {DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis},
  journal = {arXiv preprint arXiv:2601.23022},
  year = {2026},
  url = {https://arxiv.org/abs/2601.23022}
}

@inproceedings{buechel-hahn-2017-emobank,
    title = {EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis},
    author = {Buechel, Sven and Hahn, Udo},
    booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
    pages = {578--585},
    month = apr,
    year = {2017},
    address = {Valencia, Spain},
    publisher = {Association for Computational Linguistics},
    url = {https://aclanthology.org/E17-2092/},
    doi = {10.18653/v1/E17-2092}
}

@article{liang2022chineseemobank,
  author = {Lung-Hao Lee and Jian-Hong Li and Liang-Chih Yu},
  title = {Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis},
  journal = {ACM Transactions on Asian and Low-Resource Language Information Processing},
  volume = {21},
  number = {4},
  pages = {65:1--65:18},
  year = {2022},
  publisher = {Association for Computing Machinery},
  doi = {10.1145/3489141},
  url = {https://doi.org/10.1145/3489141}
}

@inproceedings{mohammad-etal-2018-semeval,
  title = {SemEval-2018 Task 1: Affect in Tweets},
  author = {Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  booktitle = {Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018)},
  pages = {1--17},
  month = jun,
  year = {2018},
  address = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/S18-1001/},
  doi = {10.18653/v1/S18-1001}
}

@inproceedings{muhammad-etal-2025-semeval,
  title = {SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection},
  author = {Muhammad, Shamsuddeen Hassan and Ousidhoum, Nedjma and Abdulmumin, Idris and Yimam, Seid Muhie and Wahle, Jan Philip and Ruas, Terry Lima and Beloucif, Meriem and De Kock, Christine and Belay, Tadesse Destaw and Ahmad, Ibrahim Said and Surange, Nirmal and Teodorescu, Daniela and Adelani, David Ifeoluwa and Fikir Aji, Alham and Ali, Fermin Dario Mario and Araujo, Vladimir and Ayele, Abinew Ali and Ignat, Oana and Panchenko, Alexander and Zhou, Yi and Mohammad, Saif M.},
  booktitle = {Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
  pages = {2558--2569},
  month = jul,
  year = {2025},
  address = {Vienna, Austria},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2025.semeval-1.327/},
  doi = {10.18653/v1/2025.semeval-1.327}
}

@inproceedings{peng2020knowing,
  title = {Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis},
  author = {Peng, Haiyun and Xu, Lu and Bing, Lidong and Huang, Fei and Lu, Wei and Si, Luo},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {34},
  number = {05},
  pages = {8600--8607},
  year = {2020},
  publisher = {AAAI Press},
  doi = {10.1609/aaai.v34i05.6383},
  url = {https://doi.org/10.1609/aaai.v34i05.6383}
}

@inproceedings{yu2016building,
  title={Building Chinese affective resources in valence-arousal dimensions},
  author={Yu, Liang-Chih and Lee, Lung-Hao and Hao, Shuai and Wang, Jin and He, Yunchao and Hu, Jun and Lai, K Robert and Zhang, Xuejie},
  booktitle={Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies},
  pages={540--545},
  year={2016}
}

@inproceedings{zhang-etal-2021-asqp,
  title = {Aspect Sentiment Quad Prediction as Paraphrase Generation},
  author = {Zhang, Wenxuan and Deng, Yang and Li, Xin and Yuan, Yifei and Bing, Lidong and Lam, Wai},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages = {9209--9219},
  month = nov,
  year = {2021},
  address = {Online and Punta Cana, Dominican Republic},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2021.emnlp-main.726/},
  doi = {10.18653/v1/2021.emnlp-main.726}
}

@ARTICLE{9676694,
  author={Feng, Ao and Zhang, Xuelei and Song, Xinyu},
  journal={IEEE Access}, 
  title={Unrestricted Attention May Not Be All You Need–Masked Attention Mechanism Focuses Better on Relevant Parts in Aspect-Based Sentiment Analysis}, 
  year={2022},
  volume={10},
  number={},
  pages={8518-8528},
  keywords={Task analysis;Sentiment analysis;Bit error rate;Deep learning;Data mining;Transformers;Semantics;Sentiment analysis;attention mechanism;pre-trained language model;masked attention},
  doi={10.1109/ACCESS.2022.3142178}}

@article{citron2020cometa,
  author    = {Francesca M. M. Citron and Mollie Lee and Nora Michaelis},
  title     = {Affective and psycholinguistic norms for German conceptual metaphors (COMETA)},
  journal   = {Behavior Research Methods},
  volume    = {52},
  number    = {3},
  pages     = {1056--1072},
  year      = {2020},
  publisher = {Springer},
  doi       = {10.3758/s13428-019-01335-6}
}

@inproceedings{mohammad-bravo-marquez-2017-wassa,
    title = "{WASSA}-2017 Shared Task on Emotion Intensity",
    author = "Mohammad, Saif  and
      Bravo-Marquez, Felipe",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      van der Goot, Erik",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-5205/",
    doi = "10.18653/v1/W17-5205",
    pages = "34--49",
    abstract = "We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best{--}worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language."
}

@inproceedings{zhang-etal-2021-aspect-sentiment,
    title = "Aspect Sentiment Quad Prediction as Paraphrase Generation",
    author = "Zhang, Wenxuan  and
      Deng, Yang  and
      Li, Xin  and
      Yuan, Yifei  and
      Bing, Lidong  and
      Lam, Wai",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.726/",
    doi = "10.18653/v1/2021.emnlp-main.726",
    pages = "9209--9219",
    abstract = "Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of cross-task transfer with the proposed unified Paraphrase modeling framework."
}

@article{yu2026semeval,
  title={SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)},
  author={Yu, Liang-Chih and Becker, Jonas and Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Lee, Lung-Hao and Lin, Ying-Lung and Wang, Jin and Wahle, Jan Philip and Ruas, Terry and Loukachevitch, Natalia and Panchenko, Alexander and Alimova, Ilseyar and Wanzare, Lilian and Odhiambo, Nelson and Gipp, Bela and Chang, Kai-Wei and Mohammad, Saif M.},
  journal={arXiv preprint arXiv:2604.07066},
  year={2026},
  doi={10.48550/arXiv.2604.07066},
  url={https://arxiv.org/abs/2604.07066}
}