Impact Score 3.63
Prof. Dr. Sebastião Pais, University of Beira Interior, Covilhã, Portugal – [email protected]
Prof. Dr. Gaël Dias, University of Caen Normandie, Caen, France [email protected]
Prof. Dr. João Cordeiro, University of Beira Interior, Covilhã, Portugal - [email protected]
Prof. Dr Mohammed Hasanuzzaman, Cork Institute of Technology, Cork, Ireland - [email protected]
Aims and Scope
Data Scientists work with tons of data, and many times that data includes natural language text. Modern organizations work with vast amounts of data. That data can come in various forms, including documents, spreadsheets, audio recordings, emails, JSON, and so many, many more. One of the most common ways that such data is recorded is via text. That text is usually quite similar to the natural language that we use from day-to-day. Natural Language Processing (NLP) is studying programming computers to process and analyze large amounts of natural textual data. Knowledge of NLP is essential for Data Scientists since it is easy to use and standard containers for storing data. Faced with performing analysis and building models from textual data, one must know how to perform the basic Data Science tasks. That includes cleaning, formatting, parsing, analyzing, visualizing, and modelling the text data. It will all require a few extra steps and the usual way these tasks are done when the data is made up of raw numbers. This kind of data needs NLP, a form of the machine learning algorithm, to analyze its contents. NLP is seen as the next big thing in data analytics that can harness big data to derive information using innovative methods to produce useful insights on current or projected market trends. NLP studies the patterns emerging in the text entries in the big data by analyzing the linguistics and semantics through statistics and machine learning and extracts the significant entities and relationships in what the customers are trying to say posts.
Essentially, instead of focusing on a word or a string of words, NLP comprehensively analyses sentences for their intent. The most common NLP methodologies are automatic summarization, disambiguation, part-of-speech tagging, relations extraction, entity extraction, and, most importantly, natural language understanding and recognition. Although research on NLP is being conducted since quite a few decades, the field has shown significant progress only in the last years. Machine learning methodologies that use NLP are now being deployed extensively across enterprises through their partner big data consulting company. Statistical Natural Language Processing (SNLP) is a field lying in the intersection of natural language processing and machine learning. SNLP differs from traditional natural language processing in that instead of having a linguist manually construct some model of a given linguistic phenomenon, that model is instead (semi-) automatically constructed from linguistically annotated resources, unsupervised and language independent. Methods for assigning part-of-speech tags to words, categories to texts, parse trees to sentences, and so on, are (semi-) automatically acquired using machine learning techniques.
In this special issue, we invite researchers and practitioners, both from academia and industry, from different disciplines and fields such as machine learning, deep learning, natural language processing, statistical learning, data mining, computational linguistics, social network analysis and other related areas to submit novel and significantly extended qualitative and quantitative research papers, that Focus on knowledge discovery in large amounts of data in data science.
Topics of Interest