% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@inproceedings{10.5555/645530.655813,
author = {Lafferty, John D. and McCallum, Andrew and Pereira, Fernando C. N.},
title = {Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data},
year = {2001},
isbn = {1558607781},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
booktitle = {Proceedings of the Eighteenth International Conference on Machine Learning},
pages = {282–289},
numpages = {8},
series = {ICML '01}
}

@misc{russell1980circumplex,
    title={A circumplex model of affect.}, 
    author={Russell, James A.},
    year={1980},
    url={https://psycnet.apa.org/record/1981-25062-001},
}

@misc{reimers2019sentencebertsentenceembeddingsusing,
      title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, 
      author={Nils Reimers and Iryna Gurevych},
      year={2019},
      eprint={1908.10084},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1908.10084}, 
}

@article{10.1023/A:1007379606734,
author = {Caruana, Rich},
title = {Multitask Learning},
year = {1997},
issue_date = {July 1997},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {28},
number = {1},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1007379606734},
doi = {10.1023/A:1007379606734},
abstract = {Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. This paper reviews prior work on MTL, presents new evidence that MTL in backprop nets discovers task relatedness without the need of supervisory signals, and presents new results for MTL with k-nearest neighbor and kernel regression. In this paper we demonstrate multitask learning in three domains. We explain how multitask learning works, and show that there are many opportunities for multitask learning in real domains. We present an algorithm and results for multitask learning with case-based methods like k-nearest neighbor and kernel regression, and sketch an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.},
journal = {Mach. Learn.},
month = jul,
pages = {41–75},
numpages = {35},
keywords = {backpropagation, generalization, inductive transfer, k-nearest neighbor, kernel regression, multitask learning, parallel transfer, supervised learning}
}

@inproceedings{barnes-etal-2022-semeval,
    title = "{S}em{E}val 2022 Task 10: Structured Sentiment Analysis",
    author = "Barnes, Jeremy  and
      Oberlaender, Laura  and
      Troiano, Enrica  and
      Kutuzov, Andrey  and
      Buchmann, Jan  and
      Agerri, Rodrigo  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.180/",
    doi = "10.18653/v1/2022.semeval-1.180",
    pages = "1280--1295",
    abstract = "In this paper, we introduce the first SemEval shared task on Structured Sentiment Analysis, for which participants are required to predict all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity. This new shared task includes two subtracks (monolingual and cross-lingual) with seven datasets available in five languages, namely Norwegian, Catalan, Basque, Spanish and English. Participants submitted their predictions on a held-out test set and were evaluated on Sentiment Graph F1 . Overall, the task received over 200 submissions from 32 participating teams. We present the results of the 15 teams that provided system descriptions and our own expanded analysis of the test predictions."
}

@inproceedings{pontiki-etal-2014-semeval,
    title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
    author = "Pontiki, Maria  and
      Galanis, Dimitris  and
      Pavlopoulos, John  and
      Papageorgiou, Harris  and
      Androutsopoulos, Ion  and
      Manandhar, Suresh",
    editor = "Nakov, Preslav  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
    month = aug,
    year = "2014",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S14-2004/",
    doi = "10.3115/v1/S14-2004",
    pages = "27--35"
}

@inproceedings{10.1145/1014052.1014073,
author = {Hu, Minqing and Liu, Bing},
title = {Mining and summarizing customer reviews},
year = {2004},
isbn = {1581138881},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1014052.1014073},
doi = {10.1145/1014052.1014073},
abstract = {Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.},
booktitle = {Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {168–177},
numpages = {10},
keywords = {reviews, sentiment classification, summarization, text mining},
location = {Seattle, WA, USA},
series = {KDD '04}
}

@inproceedings{pang-etal-2002-thumbs,
    title = "Thumbs up? Sentiment Classification using Machine Learning Techniques",
    author = "Pang, Bo  and
      Lee, Lillian  and
      Vaithyanathan, Shivakumar",
    booktitle = "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002)",
    month = jul,
    year = "2002",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W02-1011/",
    doi = "10.3115/1118693.1118704",
    pages = "79--86"
}

@misc{becker2026dimstancemultilingualdatasetsdimensional,
      title={DimStance: Multilingual Datasets for Dimensional Stance Analysis}, 
      author={Jonas Becker and Liang-Chih Yu and Shamsuddeen Hassan Muhammad and Jan Philip Wahle and Terry Ruas and Idris Abdulmumin and Lung-Hao Lee and Nelson Odhiambo and Lilian Wanzare and Wen-Ni Liu and Tzu-Mi Lin and Zhe-Yu Xu and Ying-Lung Lin and Jin Wang and Maryam Ibrahim Mukhtar and Bela Gipp and Saif M. Mohammad},
      year={2026},
      eprint={2601.21483},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.21483}, 
}

@misc{lee2026dimabsabuildingmultilingualmultidomain,
      title={DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis}, 
      author={Lung-Hao Lee and Liang-Chih Yu and Natalia Loukashevich and Ilseyar 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},
      year={2026},
      eprint={2601.23022},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.23022}, 
}

@misc{merrick2024arcticembedscalableefficientaccurate,
      title={Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models}, 
      author={Luke Merrick and Danmei Xu and Gaurav Nuti and Daniel Campos},
      year={2024},
      eprint={2405.05374},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2405.05374}, 
}

@misc{bhargava2021generalization,
      title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, 
      author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
      year={2021},
      eprint={2110.01518},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-1908-08962,
  author    = {Iulia Turc and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {Well-Read Students Learn Better: The Impact of Student Initialization
               on Knowledge Distillation},
  journal   = {CoRR},
  volume    = {abs/1908.08962},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.08962},
  eprinttype = {arXiv},
  eprint    = {1908.08962},
  timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{10.1109/TNN.2008.2005605,
author = {Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele},
title = {The graph neural network model},
year = {2009},
issue_date = {January 2009},
publisher = {IEEE Press},
volume = {20},
number = {1},
issn = {1045-9227},
url = {https://doi.org/10.1109/TNN.2008.2005605},
doi = {10.1109/TNN.2008.2005605},
abstract = {Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function τ(G, n) ∈IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.},
journal = {Trans. Neur. Netw.},
month = jan,
pages = {61–80},
numpages = {20},
keywords = {Graphical domains, graph neural networks (GNNs), graph processing, graphical domains, recursive neural networks}
}

@inproceedings{yu-etal-2026-semeval,
	title        = {{S}em{E}val-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis ({D}im{ABSA})},
	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 Panchenko, Alexander and Alimova, Ilseyar and Chang, Kai-Wei and Wanzare, Lilian and Odhiambo, Nelson and Gipp, Bela and Mohammad, Saif M.},
	year         = 2026,
	booktitle    = {Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)},
	publisher    = {Association for Computational Linguistics}
}

@misc{devlin2018pretraining,
  abstract = {We introduce a new language representation model called BERT, which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language representation models, BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning on
both left and right context in all layers. As a result, the pre-trained BERT
model can be fine-tuned with just one additional output layer to create
state-of-the-art models for a wide range of tasks, such as question answering
and language inference, without substantial task-specific architecture
modifications.
  BERT is conceptually simple and empirically powerful. It obtains new
state-of-the-art results on eleven natural language processing tasks, including
pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering
Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1
(5.1 point absolute improvement).},
  added-at = {2020-10-04T16:16:18.000+0200},
  author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  biburl = {https://www.bibsonomy.org/bibtex/210c860e3f390c6fbfd78a3b91ab9b0af/stdiff},
  description = {BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
  interhash = {a74f4c3853d3f0340e75546639134e91},
  intrahash = {10c860e3f390c6fbfd78a3b91ab9b0af},
  keywords = {nlp},
  note = {cite arxiv:1810.04805},
  timestamp = {2020-10-04T16:16:18.000+0200},
  title = {BERT: Pre-training of Deep Bidirectional Transformers for Language
  Understanding},
  url = {http://arxiv.org/abs/1810.04805},
  year = 2018
}


@article{radford2018improving,
  added-at = {2020-07-14T16:37:42.000+0200},
  author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
  biburl = {https://www.bibsonomy.org/bibtex/273ced32c0d4588eb95b6986dc2c8147c/jonaskaiser},
  interhash = {5c343ed9a31ac52fd17a898f72af228f},
  intrahash = {73ced32c0d4588eb95b6986dc2c8147c},
  keywords = {final thema:transformer},
  timestamp = {2020-07-14T16:49:42.000+0200},
  title = {Improving language understanding by generative pre-training},
  year = 2018
}

@inproceedings{vaswani2017attention,
  added-at = {2020-07-13T17:23:43.000+0200},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  biburl = {https://www.bibsonomy.org/bibtex/2a50f62f71cf1fb5a269be40e6dec54ce/s363405},
  booktitle = {Advances in neural information processing systems},
  interhash = {c9bf08cbcb15680c807e12a01dd8c929},
  intrahash = {a50f62f71cf1fb5a269be40e6dec54ce},
  keywords = {},
  pages = {5998--6008},
  timestamp = {2020-07-13T17:23:43.000+0200},
  title = {Attention is all you need},
  url = {http://arxiv.org/abs/1706.03762},
  year = 2017
}

@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}
}
