@LanguageResource{li-etal-2021-p,
    title = "{P}-Stance: A Large Dataset for Stance Detection in Political Domain",
    author = "Li, Yingjie  and
      Sosea, Tiberiu  and
      Sawant, Aditya  and
      Nair, Ajith Jayaraman  and
      Inkpen, Diana  and
      Caragea, Cornelia",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.208",
    doi = "10.18653/v1/2021.findings-acl.208",
    pages = "2355--2365",
    keywords={langrec}

}


@LanguageResource{DBLP:journals/toit/MohammadSK17,
author = {Mohammad, Saif M. and Sobhani, Parinaz and Kiritchenko, Svetlana},
title = {Stance and Sentiment in Tweets},
year = {2017},
issue_date = {August 2017},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {17},
number = {3},
issn = {1533-5399},
url = {https://doi.org/10.1145/3003433},
doi = {10.1145/3003433},
abstract = {We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.},
journal = {ACM Trans. Internet Technol.},
month = {jun},
articleno = {26},
numpages = {23},
keywords = {text classification, tweets, opinion, sentiment, Stance, polarity}
}

@LanguageResource{mohammad-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 6: Detecting Stance in Tweets",
    author = "Mohammad, Saif  and
      Kiritchenko, Svetlana  and
      Sobhani, Parinaz  and
      Zhu, Xiaodan  and
      Cherry, Colin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S16-1003",
    doi = "10.18653/v1/S16-1003",
    pages = "31--41",
    keywords={langrec}
}


@LanguageResource{Palmer2005,
author = {Palmer, Martha and Gildea, Daniel and Kingsbury, Paul},
title = {{The Proposition Bank: An Annotated Corpus of Semantic Roles}},
year = {2005},
issue_date = {March 2005},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
volume = {31},
number = {1},
issn = {0891-2017},
url = {https://doi.org/10.1162/0891201053630264},
doi = {10.1162/0891201053630264},
abstract = {The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated.We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus.We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ''trace'' categories of the treebank.},
journal = {Computational Linguistics},
month = {March},
pages = {71--106},
numpages = {36}
}

@LanguageResource{ConceptNet2017,
author = {Speer, Robyn and Chin, Joshua and Havasi, Catherine},
title = {ConceptNet 5.5: An Open Multilingual Graph of General Knowledge},
year = {2017},
publisher = {AAAI Press},
abstract = {Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings.ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use.When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.},
booktitle = {Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
pages = {4444–4451},
numpages = {8},
location = {San Francisco, California, USA},
series = {AAAI'17}
}


@LanguageResource{RDF_OWL_WordNet,
title = "RDF/OWL Representation of WordNet",
author = "Van Assem, Mark and Gangemi, Aldo and A.T. Schreiber",
note = " Assem06c ",
year = "2006",
language = "English",
publisher = "World-Wide Web Consortium W3C",
}
