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Automatic Misogyny Detection In Social Media: A Survey, John Cardiff, Elena Shushkevich
Automatic Misogyny Detection In Social Media: A Survey, John Cardiff, Elena Shushkevich
Conference Papers
This article presents a survey of automated misogyny identification techniques in social media, especially in Twitter. This problem is urgent because of the high speed at which messages on social platforms grow and the widespread use of offensive language (including misogynistic language) in them. In this article we survey approaches proposed in the literature to solve the problem of misogynistic message recognition. These include classical machine learning models like Sup-port Vector Machine, Naive Bayes, Logistic Regression and ensembles of different classical machine learning models and deep neural networks such as Long Short-term memory and Convolutional Neural Networks. We consider results …
Extracting Drug-Drug Interactions With Character-Level And Dependency-Based Embeddings, John Cardiff, Liliya Akhtyamova
Extracting Drug-Drug Interactions With Character-Level And Dependency-Based Embeddings, John Cardiff, Liliya Akhtyamova
Conference Papers
The DDI track of TAC-2018 challenge addresses the problem of an information retrieval of drug-drug interactions on structured product labeling documents with discontinuous and overlapping entities. In this paper, we present our participation for event extraction subtask (Task 1). We used a supervised long-short-term memory (LSTM) network with conditional random fields decoding (LSTM-CRF) approach with an automatic exploring of words and characters features. Additional dependency-based information was integrated into word embeddings to allow better word representation. Our system performed with above median score.
Classifying Misogynistic Tweets Using A Blended Model: The Ami Shared Task In Ibereval 2018, John Cardiff, Elena Shushkevich
Classifying Misogynistic Tweets Using A Blended Model: The Ami Shared Task In Ibereval 2018, John Cardiff, Elena Shushkevich
Conference Papers
This article describes a possible solution for Automatic Misogyny Identification (AMI) Shared Task at IBEREVAL-2018. The proposed technique is based on combining several simpler classifiers into one more complex blended model, which classified the data taking into account the probabilities of belonging to classes calculated by simpler models. We used the Logistic Regression, Naive Bayes, and SVM classifiers. The experimental results show that blended model works better than simpler models for all three type of classification, for both binomial classification (Misogyny Identifivation, Target Classification) and multinomial classification (Misogynistic Behavior).