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Found 32 papers, 3 papers with code
DuluthNLP at SemEval-2022 Task 7: Classifying Plausible Alternatives with Pre–trained ELECTRA
This paper describes the DuluthNLP system that participated in Task 7 of SemEval-2022 on Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts.
Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction
This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph.
SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph
The phrase-level annotations were scientific term and predicate phrases from the contribution sentences.
SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph
Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i. e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building.
Duluth at SemEval-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines
We use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines.
Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression
This paper describes the Duluth systems that participated in SemEval--2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval--2020).
Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets
This paper describes the Duluth systems that participated in SemEval--2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval).
Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector
This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4.
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
Hypernym Discovery is the task of identifying potential hypernyms for a given term.
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets
This paper describes the ALANIS system that participated in Task 3 of SemEval-2018.
UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
This paper describes the UMDSub system that participated in Task 2 of SemEval-2018.
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
Hypernym Discovery is the task of identifying potential hypernyms for a given term.
Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
Our task evaluation results place us 19th of 48 systems in the English evaluation, and 5th of 21 in the Spanish.
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!
Humor is a defining characteristic of human beings.
Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich Semantic Taxonomies
This paper describes the Duluth systems that participated in Task 14 of SemEval 2016, Semantic Taxonomy Enrichment.
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection
This paper describes the Duluth system that participated in SemEval-2017 Task 6 #HashtagWars: Learning a Sense of Humor.
Duluth at Semeval-2017 Task 7 : Puns upon a midnight dreary, Lexical Semantics for the weak and weary
This paper describes the Duluth systems that participated in SemEval-2017 Task 7 : Detection and Interpretation of English Puns.
Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors
Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co--occurrence frequencies or statistical measures of association to weight the importance of particular co--occurrences.
Age and Gender Prediction on Health Forum Data
Health support forums have become a rich source of data that can be used to improve health care outcomes.
