TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification

In recent years, deep learning-based models have significantly improved the Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural Network (CNN), initially used for computer vision, has shown remarkable performance for text data in various NLP problems. Most of the existing CNN-based models use 1-dimensional convolving filters n-gram detectors), where each filter specialises in extracting n-grams features of a particular input word embedding. The input word embeddings, also called sentence matrix, is treated as a matrix where each row is a word vector. Thus, it allows the model to apply one-dimensional convolution and only extract n-gram based features from a sentence matrix. These features can be termed as intra-sentence n-gram features. To the extent of our knowledge, all the existing CNN models are based on the aforementioned concept. In this paper, we present a CNN-based architecture TextConvoNet that not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. To evaluate the performance of TextConvoNet, we perform an experimental study on five text classification datasets. The results are evaluated by using various performance metrics. The experimental results show that the presented TextConvoNet outperforms state-of-the-art machine learning and deep learning models for text classification purposes.

Open-world Machine Learning: Applications, Challenges, and Opportunities

Traditional machine learning especially supervised learning follows the assumptions of closed-world learning i.e., for each testing class a training class is available. However, such machine learning models fail to identify the classes which were not available during training time. These classes can be referred to as unseen classes. Whereas, open-world machine learning deals with arbitrary inputs (data with unseen classes) to machine learning systems. Moreover, traditional machine learning is static learning which is not appropriate for an active environment where the perspective and sources, and/or volume of data are changing rapidly. In this paper, first, we present an overview of open-world learning with importance to the real-world context. Next, different dimensions of open-world learning are explored and discussed. The area of open-world learning gained the attention of the research community in the last decade only. We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for open-world machine learning. It also presents the research gaps, challenges, and future directions in open-world learning. This paper will help researchers to understand the comprehensive developments of open-world learning and the likelihoods to extend the research in suitable areas. It will also help to select applicable methodologies and datasets to explore this further.