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5 Challenges in Natural Language Processing to watch out for

    Natural Language Processing is one of the major technologies that one should watch out for. However, there are some major challenges that one must be aware of.

    Chatbot customer support, text predictions, emails, Siri, and Alexa, there are several examples of AI technology that we are using currently in our real lives. Natural Language Processing (NLP) and Machine Learning algorithms are used to interpret the data – written or spoken words – to react to the human language. NLP is an improved format to compute sentences and letters while recovering from inconsistent, fluid, or complex modes.

    Here are the major challenges around NLP that one must be aware of.

    1. 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.

    2. 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. The pre-existing NLP technologies can help in developing the product from scratch.

    3. Homonyms
    Another major challenge about NLP is Homonyms that mean words with multiple meanings. Humans can interpret the meaning behind the words that have multiple meanings according to the situation but for machines, it can be difficult to identify.

    4. 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.

    5. False Positives
    NLP can detect addressable and intelligible worlds but false positives or uncertainty is something that can be difficult for them. The developers need to create an NLP system to clear up and identify the uncertainty.