What is math about? Basically spoken, it is about uniting separate parts and bringing together something new. To understand and to read the new creation, it is important to understand the meaning of each part to create further meaning of the whole. Where is the difference to language? To properly understand a language it is important to split it into smaller units and to understand the meaning of each unit it is necessary and helpful to understand the whole language. The mathematical formula is 1+1= language.
Firstly, this seems to be confusing but lately, logical. Language does consist of smaller units which, once brought together, build up to a new system, a whole language. To understand a language it is therefore indispensable to understand logical connections but it is not necessary to be a math genius. Today’s most striking field of linguistic, Natural Language Processing (NLP), combines the ability to think logically and to analyse language in an encompassing manner.
The aim of this paper is to give a brief introduction on what is Natural Language Processing (NLP) and further, to define several challenges NLP has to face due to online data bias. The challenges which concern the field of technology as well as they influence the social impact form a work frame for the overlaying field of ethical challenges in online data which are going to be displayed in this paper. Not only existing challenges but also future solutions will be a subject of discussion.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Part One - What is NLP -
- NLP tasks before 1960.
- NLP tasks after 1960..
- Part Two - The societal impact factors of NLP -
- Avoiding exclusion.........
- Avoiding overgeneralisation
- Avoiding overexposure.....
- Underexposure and its negative impact on balanced data..
- Part Three - Major challenges for online research ethics.
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The paper aims to provide an introductory overview of Natural Language Processing (NLP) and to examine several challenges it faces due to online data bias. This includes analyzing the social impact of NLP and discussing ethical challenges within online research.
- The nature and development of Natural Language Processing (NLP)
- Societal impact factors of NLP, including issues of exclusion, overgeneralization, and overexposure.
- Ethical challenges for online research, particularly in relation to data bias and privacy.
- Potential solutions and future directions for NLP development.
- The intersection of NLP with various fields, such as computer science, linguistics, and artificial intelligence.
Zusammenfassung der Kapitel (Chapter Summaries)
The introduction highlights the connection between language and mathematics, showcasing NLP's role in understanding and manipulating language. The "What is NLP" section delves into the origins and evolution of NLP, tracing its development from early machine translation efforts to its modern applications in areas like question answering and conversational systems.
Part Two explores the social impact factors of NLP, emphasizing potential biases and the importance of avoiding exclusion, overgeneralization, and overexposure in NLP applications.
Schlüsselwörter (Keywords)
The main keywords and focus topics of the text include Natural Language Processing (NLP), online data bias, societal impact, ethical challenges, machine translation, question answering, conversational systems, exclusion, overgeneralization, overexposure, underexposure, and balanced data.
- Quote paper
- Szahel Kumke (Author), 2019, Societal Impact Factors and Major Challenges for Natural Language Processing, Munich, GRIN Verlag, https://www.grin.com/document/1112090
