{"id":6913,"date":"2023-07-24T15:49:05","date_gmt":"2023-07-24T23:49:05","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6913"},"modified":"2025-04-24T17:15:07","modified_gmt":"2025-04-24T17:15:07","slug":"the-biggest-challenges-in-nlp-and-how-to-overcome-them","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/the-biggest-challenges-in-nlp-and-how-to-overcome-them\/","title":{"rendered":"The biggest challenges in NLP and how to overcome them"},"content":{"rendered":"\n
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Joshua Hoehne<\/a> via Unsplash<\/figcaption><\/figure>\n

Humans produce so much text data that we do not even realize the value it holds for businesses and society today. We don\u2019t realize its importance because it\u2019s part of our day-to-day lives and easy to understand, but if you input this same text data into a computer, it\u2019s a big challenge to understand what\u2019s being said or happening.<\/p>\n

This is where NLP (Natural Language Processing) comes into play \u2014 the process used to help computers understand text data. However, this is not an easy task. Learning a language is already hard for us humans, so you can imagine how difficult it is to teach a computer to understand text data.<\/p>\n

Although NLP has been growing and has been working hand-in-hand with NLU (Natural Language Understanding) to help computers understand and respond to human language, the major challenge faced is how fluid and inconsistent language can be.<\/p>\n

So let\u2019s look into some of these challenges and a few solutions.<\/p>\n

Context<\/h1>\n

Context constitutes 90% of a message, words only 10%. Yes, words make up text data, however, words and phrases have different meanings depending on the context of a sentence. As humans, from birth, we learn and adapt to understand the context. Although NLP models are inputted with many words and definitions, one thing they struggle to differentiate is the context.<\/p>\n

Let\u2019s take Mandarin, for example. The language has four tones and each of these tones can change the meaning of a word. This is what we call homonyms, two or more words that have the same pronunciation but have different meanings. This can make tasks such as speech recognition difficult, as it is not in the form of text data.<\/p>\n

Solution<\/h2>\n

Embedding. This is the representation of words for text analysis. It helps a machine to better understand human language through a distributed representation of the text in an n-dimensional space. The technique is highly used in NLP challenges \u2014 one of them being to understand the context of words.<\/p>\n

There are two types of embedding techniques I will talk about here:<\/p>\n