Zobrazeno 1 - 10
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pro vyhledávání: '"Pedersen, Ted"'
Autor:
Das, Rupak Kumar, Pedersen, Ted
This paper uses the BERT model, which is a transformer-based architecture, to solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017. BERT is a very powerful large language model for classification tasks when the amount of trai
Externí odkaz:
http://arxiv.org/abs/2401.07944
Publikováno v:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), (pp. 364-376), ACL
There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search fea
Externí odkaz:
http://arxiv.org/abs/2106.07385
We use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines. Inspired by the incongruity theory of humor, we use a contrastive approach to capture the surprise in the edited headlines.
Externí odkaz:
http://arxiv.org/abs/2009.02795
Autor:
Pedersen, Ted
This paper describes the Duluth systems that participated in SemEval--2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built c
Externí odkaz:
http://arxiv.org/abs/2007.12949
Autor:
Pedersen, Ted
This paper describes the Duluth systems that participated in SemEval--2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval--2020). We participated in the three English language tasks. Our systems provide a simple M
Externí odkaz:
http://arxiv.org/abs/2007.12946
Autor:
Wang, Zhenduo, Pedersen, Ted
This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embedd
Externí odkaz:
http://arxiv.org/abs/1805.10274
Autor:
Jin, Shuning, Pedersen, Ted
This paper describes the Duluth UROP systems that participated in SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of ensembles made up of classifiers using Naive Bayes, Logistic Regression, and Random Forests. We used unig
Externí odkaz:
http://arxiv.org/abs/1805.10267
Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of
Externí odkaz:
http://arxiv.org/abs/1805.10271
Autor:
Yan, Xinru, Pedersen, Ted
Humor is a defining characteristic of human beings. Our goal is to develop methods that automatically detect humorous statements and rank them on a continuous scale. In this paper we report on results using a Language Model approach, and outline our
Externí odkaz:
http://arxiv.org/abs/1705.10272
Autor:
Pedersen, Ted
This paper describes the Duluth systems that participated in Task 14 of SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in the formal evaluation which are discussed here, along with numerous post--evaluation runs. All of
Externí odkaz:
http://arxiv.org/abs/1705.00390