You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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 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.

Cited Abstract(s):
[1]: 

[2]: This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.

[3]: Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char word n-gram methods by 18 F1 points.

[4]: Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.

[5]: Abusive messages (flames) can be both a source of frustration and a waste of time for Internet users. This paper . describes some approaches to flame recognition, mcluding a prototype system, Smokey. Smokey builds a 47-element feature vector based on the syntax and semantics of each sentence, combining the vectors for the sentences within each message. A training set of 720 messages was used by Quinlan's C4.5 decision-tree generator to determine feature-based rules that were able to correctly categorize 64 of the flames and 98 of the nonflames in a separate test set of 460 messages. Additional techniques for greater accuracy and user customization are also discussed.
One of the earliest efforts in hate speech detection can be attributed to [5] who had presented a decision tree based text classifier for web pages with a 88.2 [3] used CNN based classifiers to classify hateful tweets as racist and sexist. [4] introduced a combination of CharCNN and WordCNN architectures for abusive text classification. [1] explored four CNN models trained on character n-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams to develop a hate-speech text classification system. [2] used an ensemble of RNNs in order to identify hateful content in social media.