Natural language Processing Research Topics

Natural Language Processing (NLP), is a technology used to create interaction between humans and computer with a language which is used locally. To further know about it in detail, continue reading this research.

  1. Define NLP

NLP is used for analyzing the languages spoken by local people. For executing the NLP method, machine learning technique is used, like text-audio-text. The NLP process follows step by step guide to convert an unstructured data to a structured data which is understandable by computer; in which first it will find the rules followed in a particular natural language and then extract them. The extraction process of required information is done from the text entered in computer with the help of certain algorithms. In some cases this might not understand any piece of text data which results in an uncertain outcome.

  1. What is NLP?

NLP comes under the computer science field and in the area of Artificial Intelligence (AI), which generates communication between humans and computers. NLP helps for the computers to interpret, understand and to generate the language spoken by human in a meaningful way with using algorithms by studying, developing and implementing them.

  1. Where NLP is used?

In this section we are going to discuss about the uses of NLP. This is used by the computers to understand the human language. NLP is present in all common tools which we use every day like translation software, spam filters, Chabot’s, search engines, voice assistant, social media marketing and grammar correction. Applications of NLP is has become wide in many industries including sentiment analysis, machine translation, text classification, speech recognition and Chabot. NLP uses Tokenization technique to break a long text to simple words.

  1. Why NLP is proposed? Previous Technology Issues

Moving on to the next section, here we are going to discuss about the reason behind the proposal and challenges faced by this NLP technology. This technique was proposed in order to overcome the problems faced by this technology previously like understanding human language and interacting with them.

Some of the issues faced by this NLP technology previously are listed here:

Lack of data: For training the machine learning algorithm of NLP, it needs large amount of data. In earlier stages, collecting that much amount of data was very difficult.

Real-Time processing: Some of the NLP methods need real-time processing for coming out with good results.

Limited computing power: Computation power should be significant for NLP algorithms, but it was not enough in the earlier stages which reduces the efficiency and scalability of system.

  1. Algorithms / Protocols

After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for NLP to overcome the previous issues faced by it are: “Dependency Parsing”, “Machine Translation”, “Named Entity Disambiguation”, “Named Entity Recognition” (NER), “Part-of-Speech (POS) Tagging”, Sentiment Analysis, Text Summarization, Topic Modeling, Tokenization, Word Embedding and “Word Sense Disambiguation”.

  1. Comparative study / Analysis

Here in this section we are going to compare different algorithms related to this study in order to find the best one. The comparative studies done in NLP are: Rule-based systems, Deep learning approaches such as “Recurrent Neural Networks”, “Transformer-based models” like BERT and Machine learning models like Naive Bayes, “Support Vector Machines” and “Random Forests”.

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by NLP in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Accuracy, False positive rate, F-measure, Mean absolute error, True positive rate.

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about NLP which can be useful for you:

  1. NLP Applications

In this next section we are going to discuss about the applications of NLP. This technology has been employed in many areas, from which some of them are listed here: Chatbot, Document Classification, Language Translation, Language Understanding, Information Extraction, Language Transliteration, Named Entity Recognition (NER), Sentiment Analysis, Speech Recognition, Social Media Analysis, Text Generation, Text Summarization and Virtual Assistants.

  1. Topology

Here you are going to learn about the different choices of topologies which can be used in NLP technology. They are: “Attention-based Models”, “Bidirectional Encoder Representations from Transformers” (BERT), “Conditional Random Fields” (CRF), “CNN-RNN Hybrid”, “Encoder-Decoder Architecture”, Ensemble Models, “Feed-forward Neural Networks” (FNN), “Generative Pre-trained Transformer” (GPT), “Recurrent Neural Networks” (RNN) and Transformer.

  1. Environment

The environment in which the operation of NLP is functioning includes Data Storage, Hardware Resources, Language Support, Deployment Environment, Software Frameworks, Pre-trained Models, Data Privacy, Security and Metrics for Model Evaluation.

  1. Simulation Tools

Here we provide some simulation software for NLP system, which is established with the usage of Python, version 3.11.4.

  1. Results

After going through this research based on NLP technology which provide lot of information for you, so utilize this to clarify the doubts you have about its technology, applications of this method, and different topologies of it, algorithms followed by it also about the limitations and how it can be overcome.

Natural language Processing Research Ideas

  1. A Survey on Attention mechanism in NLP
  2. Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration
  3. Blockchain based Secure Event Management System using NLP and RNN Algorithm
  4. Analyzing the Emotions of Food Products Reviews using NLP and Adaboost Algorithm
  5. Generalizability of NLP-based Models for Modern Software Development Cross-Domain Environments
  6. The Study on NLP-based Semantic Analysis Technology to Improve the Accuracy of English Translation
  7. Theoretical Guidance in the Field of Health and Healing Using NLP
  8. An NLP-based statistical reporting methodology applied to court decisions
  9. Advanced NLP Based Entity Key Phrase Extraction and Text-Based Similarity Measures in Hadoop Environment
  10. NLP based model to convert English speech to Gujarati text for deaf & dumb people
  11. Enhancing Recommender Systems with NLP-based Biased Singular Value Decomposition
  12. Design and Development of “Virtual AI Teacher” System Based on NLP
  13. MCM-CASR: Novel Alert Correlation Framework for Cyber Attack Scenario Reconstruction Based on NLP, NER, and Semantic Similarity
  14. Applicant Screening System Using NLP
  15. NLP Research Based on Transformer Model
  16. NLP and Deep Learning Based POS Tagging and Blank Prediction Assistant
  17. NLP based Analysis and Detection of Unethical Text
  18. Visualizing Chemistry Experiments Using NLP and Computer Graphics
  19. Intelligent Email Automation Analysis Driving through Natural Language Processing (NLP)
  20. NLP driven Content Classification towards Fake News and Bully Detection
  21. Research on NOTAM Information Extraction of Civil Aviation with NLP
  22. A Novel Approach for Classifying DNA Barcodes Using Ensemble NLP Models
  23. NLP Based Hate Speech Detection and Moderation
  24. Text Summarization of Amazon Customer Reviews using NLP
  25. Unveiling the Post-Covid Economic Impact Using NLP Techniques
  26. Recognition and Processing of phishing Emails Using NLP: A Survey
  27. Detection and Analyzing Phishing Emails Using NLP Techniques
  28. AI Enabled- Information Retrieval Engine (AI-IRE) in Legal Services: An Expert-Annotated NLP for Legal Judgments
  29. Korean Language NLP Model Based Emotional Analysis of LGBTQ Social Media Communities
  30. Smart Patient Records using NLP and Blockchain
  31. Consumer review Analysis using NLP and Data Mining
  32. A Mental Health Chatbot Delivering Cognitive Behavior Therapy and Remote Health Monitoring Using NLP and AI
  33. NLP-Based Next-Word Prediction Model
  34. Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices
  35. Matching Products with Deep NLP Models
  36. Research on NLP Based Chinese English Translation Text Quality Rating Model
  37. AI-powered Chatbot for Improved Customer Feedback Management using NLP
  38. Analysis of hybrid combination of NLP and AI for any kind of applications
  39. Artificial Intelligence’s Function in Chatbot That Use NLP and SVM Algorithms
  40. Intelligent Healthcare: Using NLP and ML to Power Chabot for Improved Assistance
  41. Link Prediction on Graphs Using NLP Embedding
  42. Vocals – An App for Vocally Impaired using NLP Conversational Model
  43. GUI: An Interface for Hate Speech Detection using NLP Technique
  44. Constructing the Terminological Core of NLP Ontology
  45. Balancing Robustness and Covertness in NLP Model Watermarking: A Multi-Task Learning Approach
  46. Research on Improving Personalized Recommendation Accuracy Based on NLP Semantic Analysis
  47. Threat Land: Extracting Intelligence from Audit Logs via NLP methods
  48. A Survey of Text Representation and Embedding Techniques in NLP
  49. TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
  50. Advanced NLP Framework for Text Processing