Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification

Doostmohammadi, Ehsan; Sameti, Hossein; Saffar, Ali

doi:10.18653/v1/s19-2110

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Tonal and non-tonal languages [3,7] are distinguished by LID systems. Deep learning neural network model [4,9,13,14,15,20] is developed to perform LID. LID system is developed by using integration of MFCC with its shifted delta cepstrum as features and deep belief networks [5] for modeling and classi cation.…”

Section: Analysis Of Speech Uttered In Different Languagesmentioning

confidence: 99%

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

2023

Preprint

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Recognizing linguistic information from speech has found applications in interpretation of language in which the utterance is spoken and the system could be used as a translator to convert sentence spoken in one language into another language meaningfully. Real time implementation of language identification (LID) from speech requires the speech to be fed from the Raspberry Pi board used in the transmitter section and the Raspberry Pi board in the receiver section receives it and given to the system for identifying the language of the speech. This system requires the training phase in which two dimensional spectrogram features are derived from the training set of speeches and given to the CNN layered architecture for creating templates for languages. Testing phase involves the transmission of speech from the memory card of the Raspberry Pi board in transmitter system. Raspberry Pi board in the receiver receives it and given to the system in receiver section. Two dimensional spectrogram features are derived for test speech and given to the CNN templates and based on the similarity index, test language is interpreted. This system is implemented using spectrogram, Melspectrogram and ERB spectrogram as features and CNN for modeling and classification of languages. Validation error is 1.4%, 1.8% and 3% for spectrogram, Melspectrogram and ERB spectrogram based systems respectively and decision level fusion classifier gives 0.9% as validation error. This system can be implemented in hardware by using Raspberry Pi board. This automated real time multilingual language identification system would be useful in forensic department and defense sectors to identify the persons belonging to any region or speaking in any language.

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Section: Analysis Of Speech Uttered In Different Languagesmentioning

confidence: 99%

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

2023

Preprint

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“…In another study, Doostmohammadi et al (2019) designed a deep learning method for hate speech identification in Twitter messages. The designed deep learning method includes feature representation models for tweets feature extraction.…”

Section: Related Work 21 Literature Reviewmentioning

confidence: 99%

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Ayo

Folorunso

Ibharalu

et al. 2020

IJICC

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PurposeHate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.Design/methodology/approachThis study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.FindingsThe proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implicationsFinally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/valueThe main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.

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Identifying Offensive Content in Social Media Posts

Singh

Ray

2021

Combating Online Hostile Posts in Regional Languages During Emergency Situation

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Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification

Cited by 8 publications

References 19 publications

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Identifying Offensive Content in Social Media Posts

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