Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora

© 2018 The Authors. Published by Elsevier B.V. How can we know what is going on in the world with a click of a button? With the increase of digital data everywhere, it is becoming difficult to categorize and retrieve information from such huge data. Topic detection is considered a powerful way to mine data and relate similar documents together. Although the Arabic content on the web is increasing every day, the application of topic detection on Arabic text is not up to this increase. In this paper we are investigating famous topic detection techniques, and latest significant scholarly articles related to topic detection in general and in the Arabic domain in specific. This survey paper will help researchers interested in the domain of topic detection to be familiar with commonly used techniques and updated with the latest technologies in this area

BERT BiLSTM-Attention Similarity Model

Semantic similarity models are a core part of many of the applications of natural language processing (NLP) that we may be encountering daily, which makes them an important research topic. In particular, Question Answering Systems are one of the important applications that utilize semantic similarity models. This paper aims to propose a new architecture that improves the accuracy of calculating the similarity between questions. We are proposing the BERT BiLSTM-Attention Similarity Model. The model uses BERT as an embedding layer to convert the questions to their respective embeddings, and uses BiLSTM-Attention for feature extraction, giving more weight to important parts in the embeddings. The function of one over the exponential function of the Manhattan distance is used to calculate the semantic similarity score. The model achieves an accuracy of 84.45% in determining whether two questions from the Quora duplicate dataset are similar or not