Challenges and Limitations in NLP

Natural Language Processing (NLP) has made significant strides, but it still faces various challenges and limitations that researchers and developers must address. Understanding these challenges is crucial for advancing the field and developing more effective NLP solutions. Some of the key challenges and limitations in NLP include:

#1. Language differences

In the United States, most people speak English, but if you’re thinking of reaching an international and/or multicultural audience, you’ll need to provide support for multiple languages.

Different languages have not only vastly different sets of vocabulary, but also different types of phrasing, different modes of inflection, and different cultural expectations. You can resolve this issue with the help of “universal” models that can transfer at least some learning to other languages. However, you’ll still need to spend time retraining your NLP system for each language.

#2. Irony and sarcasm

Irony and sarcasm present problems for machine learning models because they generally use words and phrases that, strictly by definition, may be positive or negative, but actually connote the opposite.

#3. Ambiguity

Ambiguity in NLP refers to sentences and phrases that potentially have two or more possible interpretations.

Lexical ambiguity: A word that could be used as a verb, noun, or adjective.
Semantic ambiguity: The interpretation of a sentence in context. For example: I saw the boy on the beach with my binoculars. This could mean that I saw a boy through my binoculars or the boy had my binoculars with him
Syntactic ambiguity: In the sentence above, this is what creates the confusion of meaning. The phrase with my binoculars could modify the verb, “saw,” or the noun, “boy.”

#4. slang

Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP – especially for models intended for broad use. Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day.

#5. moving conversation

Many modern NLP applications are built on dialogue between a human and a machine. Accordingly, your NLP AI needs to be able to keep the conversation moving, providing additional questions to collect more information and always pointing toward a solution.

#6. Words with multiple meanings

No language is perfect, and most languages have words that have multiple meanings. For example, a user who asks, “how are you” has a totally different goal than a user who asks something like “how do I add a new credit card?” Good NLP tools should be able to differentiate between these phrases with the help of context.

#7. Innate Biases

In certain situations, the biases of its programmers, and also biases in the data sets used to develop them, might be carried by NLP systems. It’s difficult to create a solution that operates in all situations and with all the people.

#8. Training Data

NLP is mainly about studying the language and to be proficient, it is essential to spend a substantial amount of time listening, reading, and understanding it. NLP systems focus on skewed and inaccurate data to learn inefficiently and incorrectly.

#9. Development Time

The total time taken to develop an NLP system is higher. AI evaluates the data points to process them and use them accordingly. The GPUs and deep network work on training the datasets that can be reduced by a few hours.

#10. Misspellings

It is not uncommon for humans to make spelling mistakes that can be difficult to interpret. The machine needs to detect the work properly and hence it is essential to employ NLP technology to progress and identify the misspellings.

#11. Phrases with multiple intentions

Some phrases and questions actually have multiple intentions, so your NLP system can’t oversimplify the situation by interpreting only one of those intentions. For example, a user may prompt your chatbot with something like, “I need to cancel my previous order and update my card on file.” Your AI needs to be able to distinguish these intentions separately.