Challenges and Considerations of Natural Language Processing Techniques
Despite its advancements, the domain of NLP have several challenges that includes:
- Context Understanding: A lot of times, understanding and capturing the context of a particular conversation or text is very difficult, but this is very important for accurate interpretation of the models.
- Ambiguity: There are many human languages in the world and they are often ambiguous, with different languages having words that have multiple various meaning based on the context.
- Data Privacy: It is very challenging to ensure the privacy of the user data, in the case of processing large volumes of personal text data of theirs.
- Language Diversity: This is considered to be most challenging task, that is to develop the machine learning models that work accurately for various languages and dialects.
Natural Language Processing (NLP): 7 Key Techniques
Natural Language Processing (NLP) is a subfield in Deep Learning that makes machines or computers learn, interpret, manipulate and comprehend the natural human language. Natural human language comes under the unstructured data category, such as text and voice. Generally, computers can understand the structured form of data, such as tables and spreadsheets in the database, since the human language, like text and voice, comes under the category of unstructured data, and since computers can’t understand the unstructured textual or voice data, NLP comes into action.
In this article, we will explore about 7 Natural Language Processing Techniques that form the backbone of numerous applications across various domains.