Ranking Relevance in Yahoo Search

Yin, Dawei; Hu, Yu; Tang, Jiliang; Daly, Tim; Zhou, Mianwei; Ouyang, Hua; Chen, Jianhui; Kang, Changsung; Deng, Hongbo; Nobata, Chikashi; Langlois, Jean-Marc; Chang, Yi

doi:10.1145/2939672.2939677

Cited by 109 publications

(49 citation statements)

References 25 publications

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“…One of the earliest efforts in hate speech detection can be attributed to (Spertus, 1997) who had presented a decision tree based text classifier for web pages with a 88.2 % accuracy. Contemporary works on Yahoo news pages were done (Sood et al, 2012), and later taken up by (Yin et al, 2016). (Xiang et al, 2012) detected offensive tweets using logistic regression over a tweet dataset with the help of a dictionary of 339 offensive words.…”

Section: Related Workmentioning

confidence: 99%

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Mahata¹,

Zhang²,

Uppal³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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In this paper, we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub-task A is based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B, we rely on a set of heuristics derived from the training data and manual observation. We provide a detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub-task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in Sub Task B achieving a macro F1 of 0.695.

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Section: Related Workmentioning

confidence: 99%

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Mahata¹,

Zhang²,

Uppal³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

View full text Add to dashboard Cite

show abstract

“…One of the earliest efforts in hate speech detection can be attributed to Spertus (1997) who had presented a decision tree based text classifier for web pages with a remarkable 88.2 % accuracy. Contemporary works on Yahoo news pages were done Sood et al (2012) and later taken up by Yin et al (2016a) . Xiang et al (2012) detected offensive tweets using logistic regression over a tweet dataset with the help of a dictionary of 339 offensive words.…”

Section: Related Workmentioning

confidence: 99%

Detecting Offensive Tweets in Hindi-English Code-Switched Language

Mathur

Shah

Sawhney

et al. 2018

Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

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The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: nonoffensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.

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“…Learning to rank methods [12,16,23,35,36,43,48,50,60,65] have been widely used in the recommendation and ranking literatures, primarily to re-rank a small shortlist of items which has been generated by simple heuristics like tf-idf scoring or by more scalable approaches like extreme classifiers or XReg. These rankers usually have super-linear dependence on the number of labels and hence do not scale to XR.…”

Section: Related Workmentioning

confidence: 99%

Extreme Regression for Dynamic Search Advertising

Prabhu

Kusupati

Gupta

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large number of labels to a data point. XR can provide elegant solutions to many large-scale ranking and recommendation applications including Dynamic Search Advertising (DSA). XR can learn more accurate models than the recently popular extreme classifiers which incorrectly assume strictly binary-valued label relevances. Traditional regression metrics which sum the errors over all the labels are unsuitable for XR problems since they could give extremely loose bounds for the label ranking quality. Also, the existing regression algorithms won't efficiently scale to millions of labels. This paper addresses these limitations through: (1) new evaluation metrics for XR which sum only the k largest regression errors; (2) a new algorithm called XReg which decomposes XR task into a hierarchy of much smaller regression problems thus leading to highly efficient training and prediction. This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks.Experiments on benchmark datasets demonstrated that XReg can outperform the state-of-the-art extreme classifiers as well as large-scale regressors and rankers by up to 50% reduction in the new XR error metric, and up to 2% and 2.4% improvements in terms of the propensity-scored precision metric used in extreme classification and the click-through rate metric used in DSA respectively. Deployment of XReg on DSA in Bing resulted in a relative gain of 27% in query coverage. XReg's source code can be downloaded from [1]

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Ranking Relevance in Yahoo Search

Cited by 109 publications

References 25 publications

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Detecting Offensive Tweets in Hindi-English Code-Switched Language

Extreme Regression for Dynamic Search Advertising

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