In this paper, we present the results obtained by BERT, BiLSTM and SVM classifiers on the shared task on Sarcasm Detection held as part of The Second Workshop on Figurative Language Processing. The shared task required the use of conversational context to detect sarcasm. We experimented by varying the amount of context used along with the response (response is the text to be classified). The amount of context used includes (i) zero context, (ii) last one, two or three utterances, and (iii) all utterances. It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set. On the other hand, the best performance for the Reddit data set was obtained when using only the response without any contextual information. The BERT classifier obtained F-score of 0.743 and 0.658 for the Twitter and Reddit data set respectively.

In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled "HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter". This paper presents the results obtained for Subtask A for English language. The results of the BiLSTM and LR models are compared for two different types of preprocessing. One with no stemming performed and no stopwords removed. The other with stemming performed and stopwords removed. The BiLSTM model without attention performed the best for the first test, while the LR model with character n-grams performed the best for the second test. The BiLSTM model obtained an F1 score of 0.51 on the test set and obtained an official ranking of 8/71.

Named Entity Recognition is always important when dealing with major Natural Language

Steganalysis is the art of detecting hidden messages embedded inside Steganographic Images. Steganalysis involves detection of steganography, estimation of message length and its extraction. Recently Steganalysis receives great deal of attention from the researchers due to the evolution of new, advanced and much secured steganographic methods for communicating secret information. This paper presents a universal steganalysis method for blocking recent steganographic techniques in spatial domain. The novel method analyses histograms of both the cover and suspicious image and based on the histogram difference it gives decision on the suspicious image of being stego or normal image. This method for steganalysis extracts a special pattern from the histogram difference of the cover and . By finding that specific pattern from the histogram difference of the suspicious and cover image it detects the presence of hidden message. The proposed steganalysis method has been experimented on a set of stego images where different steganographic techniques are used and it successfully detects all those stego images.

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