Unravelling the Power of Natural Language Processing in Diverse Industries
Natural Language Processing and its Real-World Applications
Natural language processing, or NLP, is a sub-domain of artificial intelligence that deals with the analysis of human language. It’s a growing field with many practical business applications. However, larger companies that have their own data science departments often lack in-house NLP specialists, and may need NLP consultants.
Through NLP, unstructured text documents can be interpreted by machines. This could be industry-specific reports like pharmaceutical or maritime accident reports, or more general text such as emails or customer feedback.
NLP: Tasks and Applications
NLP has many practical implementations across various domains:
Machine Translation: Tools like Google Translate employ NLP for language translation.
Virtual Assistants: Siri, Alexa, and Google Assistant use NLP for speech recognition, dialogue management, and more.
Consulting Projects: As an NLP consultant, tasks such as machine learning modeling, theme identification in pharmaceutical company interviews, text survey responses scaling, railway incident reports analysis, and more can be covered.
In essence, NLP is a combination of Natural Language Understanding, or NLU (translating human-readable text into structured data), and Natural Language Generation, or NLG (converting structured data into human-readable text).
Learn more about such applications and details on Fast Data Science’s page about What NLP is used for.
The Mechanism
Traditionally, the NLP pipeline starts with a tokenizer that splits the text into tokens. These tokens are then passed through components that append additional information, for example, the part-of-speech of the word, the lemma or stem, tagging important entities (named entity recognition), and sometimes generating a parse tree of the sentence.
However, modern approaches to NLP are moving away from the conventional pipeline structure. Transformer models, like BERT and GPT-3, are prevalent in NLP tasks today due to their capacity to handle sequences, making them ideal for processing text.
These models are intricate and challenging to train, so developers often resort to pre-built libraries like Hugging Face or Open AI.
Despite what the AI-driven approach may suggest, successful NLP applications usually require domain understanding and industry expertise rather than complex deep learning networks.
Head over to Fast Data Science to learn more about leveraging NLP for your business needs.
