Trends
NLP techniques in data science
Application of NLP, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future.
Headline
Application of NLP, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future.
Context
Natural Language Processing ( NLP ) is a prominent branch of artificial intelligence (AI) within data science, dedicated to extracting insights from textual data. This has led to a surge in demand for NLP professionals, as every conversation and expression harbors valuable information crucial for decision-making. However, extracting insights from text data presents a formidable challenge, given the myriad languages, expressions, and tones humans employ. The data generated from our daily interactions is inherently unstructured. Yet, advancements in data science and NLP techniques have enabled machines to engage in meaningful conversations with humans. In this article, we’ll explore and delve into the ten most widely used NLP techniques in data science.
Evidence
Pending intelligence enrichment.
Analysis
Also read: The difference between Conversational AI and GenAI Tokenisation, a fundamental NLP technique, involves segmenting text into sentences and words, essentially dividing it into tokens. This process eliminates certain characters like punctuation and hyphens to render the text more analytically manageable. Consider this example: when tokenising, the text is typically divided by blank spaces. However, issues may arise, particularly with punctuation. For instance, in the case of abbreviations like “Mr.,” the period should ideally be retained as part of the same token, but tokenisation may erroneously split it into two words. This challenge becomes more pronounced in domains with complex biomedical text containing numerous hyphens, parentheses, and punctuations, leading to potential complications during tokenisation. Also read: Exploring the best conversational AI platforms
Key Points
- Natural Language Processing it is a branch of data science that focuses on training computers to process and interpret conversations in text format in a way humans do by listening.
- NLP applications are difficult and challenging during development as computers require humans to interact with them using programming languages like Java, Python, etc., which are structured and unambiguous.
- Application of natural language processing, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future.
Actions
Pending intelligence enrichment.
