NLP Problems


Neural networks today can speak, write, listen and understand thanks to natural language processing (NLP). It extracts values ​​from messages and structures them. But it is still difficult for us to find a common language with machines. Researchers are working to ensure the computer can carry on a conversation, joke, and puns.

Artificial intelligence can quickly isolate such signals — recognize and repeat them. Nevertheless, it takes hundreds of years to form such a stable marker. In the same way, computer systems today determine the emotional coloring almost unmistakably from our speech. However, the more precisely we try to teach the computer to understand speech, the deeper we dive into the details, and the more complexities appear.

However, NLP cannot realize absolutely everything that human speech is capable of. There are so many problems that NLP faces, and the main reason is the difference between machines and humans. Human speech is difficult for a computer to repeat or understand. It creates a lot of problems, and modern developers are trying to minimize them.

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The essence of Natural Language Processing

Natural language processing is a proper discipline concerned with machines’ understanding, processing, and production of natural language. NLP is actually at the interface between computer science and linguistics. The device can interact directly with humans.

Natural language processing is not an easy problem for artificial intelligence. It is mainly due to the nature of human language, which is very complex. Numerous rules and relationships make it problematic for computers to understand and interpret speech correctly. One example is sarcastic sayings. Those are nlp problems that are extremely difficult for the computer to understand. On the other hand, there are numerous easy tasks, such as words or recognizing plurals, that a computer can learn.

To understand natural language, the computer must understand words, concepts and rules. It is often very easy for us humans, but this is an excellent challenge of natural language processing.

The problem of finding meanings

Language is not only a system of rules but also exceptions. These are also emotions, gestures, cultural and everyday context, metaphors, puns, or sarcasm. The computer easily translates from English into Swahili, but the problem is it does not know either one or the other. Therefore, the main task of researchers is to teach algorithms to extract meanings from words and work with images. A person remembers the purpose of the statement and not its form.

Semantic analysis of the text is responsible for extracting meaning. The correct use of words in context provides pragmatic analysis. There are other levels of natural language processing:

  • morphological,
  • syntactic,
  • phonological.

But the algorithms are already coping with them.

NLP Problems

What are the prospects and challenges of NLP?

The rules governing the transformation of raw text into information are not easy for computers to understand. You need to understand both the words and how the concepts are related to conveying the desired message.

Key issues include:

  • Ambiguity. In natural language, words are unambiguous but have different meanings in different contexts, resulting in lexical, syntactic, and semantic ambiguity. The NPL offers several methods to solve nlp problems, for instance, context evaluation. However, the understanding of the semantic meaning of the words in the sentence is not fully developed.
  • Synonymy. Another critical phenomenon of natural language is that we express the same idea in different terms, depending on the specific context.
  • Coreference. Coreference aims to find all expressions which refer to the same object. It is an essential step to many complex NLP tasks which require understanding the entire text, such as summarizing documents, answering questions, and extracting information. The problem has been revived with the advent of modern deep learning techniques.
  • Writing style. Depending on the author’s personality, intentions, and feelings, the same thought can be expressed in different ways. Some writers are not shy about using irony or sarcasm to convey the opposite of the literal meaning.

While humans pick up a language quickly, the ambiguity and inaccuracy of natural languages ​​make it difficult for machines to implement NLP.

Model performance

In practice, it is often challenging to assess the quality of the model results. Interpreting emotion classifications is far easier than evaluating summaries. Therefore, it is essential to determine a quality standard, especially in the initial phase of a project. On the one hand, this should be practical and, on the other hand, it should reflect the problem to be solved.

In addition, questions about the model performance should be clarified. A model that calculates results at large time intervals has different properties than a model that has to answer queries within tenths of a second.


Pre-trained models (transfer learning) have the property that they often perform well in various tasks. As a result, these models often provide good but not outstanding insights. Depending on the business case, the model should be fine-tuned so optimal results related to the problem are generated.

Fine-tuning involves two dimensions. On the one hand, the model should be adapted to language units. Vocabulary, slang, or dialect play a decisive role. Depending on the industry, these areas vary greatly.

On the other hand, the model should also be specifically tailored to the business problem at hand in addition to fine-tuning. To classify emotions, a model must work differently than when used to translate texts.

Challenges are in the nature of language

Natural language processing technology faces various challenges when it comes to analyzing human language. Individual words are not a problem to the machine because it accesses the database there — you understand this as a kind of dictionary.

The real problem, however, lies in the nature of the language. The sentences can usually only be understood correctly in context when we say something. There are numerous ways of saying something and vice versa. A sentence can have many different meanings. Understanding the context is relatively easy for us as humans because we can judge statements based on our life experiences. The machine has to learn it first.


Natural language processing deals with understanding human language — and that’s where the big problem lies. The language is very complex and not always clear. In addition, evaluating human language is made even more difficult by things like idioms, irony, or puns.

It is not enough for the computer to recognize individual words. The sentences and contexts that the machine has to understand are crucial. Man has the advantage of looking back on his life experience and learning the language almost from birth. Therefore, language is easy for humans to understand and classify. On the other hand, the computer must first learn all basics with the help of algorithms.

It is now part of reality: man and machine communicate with each other. Due to the ever-increasing computing power and a large amount of data, the action of natural language processing technologies is getting better and better.

As the various examples show, natural language processing is primarily used to improve interaction with the customer. NLP systems are characterized by the fact that they are available at all times and can be scaled as required.