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Research Topics in Natural Language Processing Algorithms and Applications

Research Topics in Natural Language Processing Algorithms and Applications

  Natural Language Processing in machine learning employs computational linguistics for learning, analyzing, understanding, and generating human language content. The processing of natural language involves many techniques include morphological analysis, lexical analysis, syntactic analysis, semantic analysis, discourse analysis, and pragmatic analysis. Support Vector Machines, Bayesian networks, Maximum Entropy, and Conditional Random fields are the most commonly used machine learning algorithms for Natural Language Processing.

  The popular applications of Natural Language Processing are information retrieval, information extraction, machine translation, sentiment analysis, text simplification and summarization, question answering systems, chatbot systems, natural language text processing, speech recognition, and spam filter. In particular, it is useful for spell check, auto-correction, text simplification, and topic classification. The advancement of Natural language processing over machine learning is the discovery of transformations and transfer learning.

  • NLP allows scholars to easily extract potential insights contained in textual information while avoiding burdensome computational work.

  • NLP enhances automatic keyphrase generation methods with prior knowledge and without prior knowledge of data to predict semantic meaning and absent keyphrases.

  • NLP captures the complexity of the text language and enriches traditional information to enhance business products and services.

  • The NLP algorithms can identify understudied fields and opportunities for breakthroughs.

  • In the real-life world, every internet user has experienced NLP applications by using NLP techniques such as natural language understanding (NLU) and natural language generation (NLG)

  • NLP in AI there are many clear advantages for the organization:

  Better data analysis - With the help of NLP technology, large amounts of text-based information can be processed and analyzed. Deep learning models can be applied to NLP tasks to enhance the recruiting processes even more effectively.

  Streamlined processes - With the help of NLP systems, a chatbot can be trained to find specific clauses across multiple documents without human intervention.