Are Word Embedding-based Features Useful for Sarcasm Detection?

Joshi, Aditya; Tripathi, Vaibhav; Patel, Kevin; Bhattacharyya, Pushpak; Carman, Mark James

doi:10.18653/v1/d16-1104

Cited by 160 publications

(94 citation statements)

References 17 publications

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“…Word embeddings have proved to be useful for various tasks, such as Part of Speech Tagging (Collobert and Weston, 2008), Named Entity Recognition Sentence Classification (Kim, 2014), Sentiment Analysis (Liu et al, 2015), Sarcasm Detection (Joshi et al, 2016). Medical domain specific pre-trained word embeddings were released by different groups, such as Pyysalo et al (2013), Brokos et al (2016), etc.…”

Section: Related Workmentioning

confidence: 99%

Adapting Pre-trained Word Embeddings For Use In Medical Coding

Patel¹,

Patel²,

Golakiya³

et al. 2017

BioNLP 2017

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Word embeddings are a crucial component in modern NLP. Pre-trained embeddings released by different groups have been a major reason for their popularity. However, they are trained on generic corpora, which limits their direct use for domain specific tasks. In this paper, we propose a method to add task specific information to pre-trained word embeddings. Such information can improve their utility. We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings. We adapt CBOW algorithm from the word2vec package for our purpose. We evaluated our approach on five different pre-trained word embeddings. Both the original word embeddings, and their modified versions (the ones with added information) were used for automated review of medical coding. The modified word embeddings give an improvement in f-score by 1% on the 5-fold evaluation on a private medical claims dataset. Our results show that adding extra information is possible and beneficial for the task at hand.

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Section: Related Workmentioning

confidence: 99%

Adapting Pre-trained Word Embeddings For Use In Medical Coding

Patel¹,

Patel²,

Golakiya³

et al. 2017

BioNLP 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The researchers combined lexical with sentiment, syntactic and semantic Word2Vec cluster features for irony detection in English tweets using an SVM and obtained a top F 1 -score of 68%. Similarly, Joshi et al (2016) used an SVM classifier and expanded their set of lexical and sentiment features with different word embedding features. They showed that incorporating Word2Vec and dependency weight-based word embeddings results the most beneficial for irony detection, yielding F-scores of up to 81%.…”

Section: Computational Approaches To Ironymentioning

confidence: 99%

Exploring the fine-grained analysis and automatic detection of irony on Twitter

Hee

Lefever

Hoste

2018

Lang Resources & Evaluation

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To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme that allows for identification of different types of irony. We conduct a series of binary classification experiments for automatic irony recognition using a support vector machine (SVM) that exploits a varied feature set and compare this method to a deep learning approach that is based on an LSTM network and (pre-trained) word embeddings. Evaluation on a held-out corpus shows that the SVM model outperforms the neural network approach and benefits from combining lexical, semantic and syntactic information sources. A qualitative analysis of the classification output reveals that the classifier performance may be further enhanced by integrating implicit sentiment information and context-and user-based features.

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“…Due to this neutrality, the lexicon based methods are unable to capture the incongruity present. Therefore, maximum and minimum GloVe (Pennington et al, 2014) cosine similarity between any two words in a tweet are used as features in our system (Joshi et al, 2016).…”

Section: Feature Extractionmentioning

confidence: 99%

NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets

Rangwani¹,

Kulshreshtha

Singh

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes our participation in SemEval 2018 Task 3 on Irony Detection in Tweets. We combine linguistic features with pre-trained activations of a neural network. The CNN is trained on the emoji prediction task. We combine the two feature sets and feed them into an XGBoost Classifier for classification. Subtask-A involves classification of tweets into ironic and non-ironic instances, whereas Subtask-B involves classification of tweets into non-ironic, verbal irony, situational irony or other verbal irony. It is observed that combining features from these two different feature spaces improves our system results. We leverage the SMOTE algorithm to handle the problem of class imbalance in Subtask-B. Our final model achieves an F1-score of 0.65 and 0.47 on Subtask-A and Subtask-B respectively. Our system ranks 4 th on both tasks, respectively, outperforming the baseline by 6% on Subtask-A and 14% on Subtask-B.

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Are Word Embedding-based Features Useful for Sarcasm Detection?

Cited by 160 publications

References 17 publications

Adapting Pre-trained Word Embeddings For Use In Medical Coding

Adapting Pre-trained Word Embeddings For Use In Medical Coding

Exploring the fine-grained analysis and automatic detection of irony on Twitter

NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets

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