SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition
This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examine a variety of word association features derived from University of Southern Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus (EAT) and find that word association-based features outperform Word2Vec similarity, a popular semantic relatedness measure. Our system achieves an accuracy of 56.42{\%} using a combination of unigram perplexity, bigram perplexity, EAT difference (tweet-avg), USF forward (max), EAT difference (word-avg), USF difference (word-avg), EAT forward (min), USF difference (tweet-max), and EAT backward (min).
Code (0)
등록된 구현이 없습니다.
Tasks
AvgSimilar Papers 제목 키워드 기반
Recognizing Humour using Word Associations and Humour Anchor Extraction
This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models. Specifically, we ex…
Semantic Textual SimilarityWord EmbeddingsBuhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases
This paper describes a system that aims at assessing humour intensity in edited news headlines as part of the 7th task of SemEval-2020 on {``}Humor, Emphasis and Sentiment{''}. Various factors need to be accounted for in…
Language ModelingLanguage ModellingregressionIIITH at SemEval-2021 Task 7: Leveraging transformer-based humourous and offensive text detection architectures using lexical and hurtlex features and task adaptive pretraining
This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense. Our results focus on two major objectives: (i) Effect of task adaptive pretraining on the performanc…
Text DetectionThe exception of humour: Iconicity, Phonemic Surprisal, Memory Recall, and Emotional Associations
This meta-study explores the relationships between humor, phonemic bigram surprisal, emotional valence, and memory recall. Prior research indicates that words with higher phonemic surprisal are more readily remembered, s…
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes
In this paper, we aim at learning the relationships and similarities of a variety of tasks, such as humour detection, sarcasm detection, offensive content detection, motivational content detection and sentiment analysis …
AllMulti-Task LearningSarcasm DetectionSentiment Analysis