As artificial intelligence apes the human speech, vision, and mind patterns, the domain of NLP is buzzing with some key developments in place.

NLP is one of the most crucial components for structuring a language-focused AI program, for example, the chatbots which readily assist visitors on the websites and AI-based voice assistants or VAs. NLP as the subset of AI enables machines to understand the language text and interpret the intent behind it by various means. A hoard of other tasks is being added via NLP like sentiment analysis, text classification, text extraction, text summarization, speech recognition, and auto-correction, etc.

However, NLP is being explored for many more tasks. There have been many advancements lately in the field of NLP and also NLU (natural language understanding) which are being applied on many analytics and modern BI platforms. Advanced applications are using ML algorithms with NLP to perform complex tasks by analyzing and interpreting a variety of content.

About NLP and NLP tasks

Apart from leveraging the data produced on social media in the form of text, image, video, and user profiles, NLP is working as a key enabler with the AI programs. It is heightening the application of Artificial Intelligence programs for innovative usages like speech recognition, chatbots, machine translation, and OCR or optical character recognition. Often the capabilities of NLP are turning the unstructured content into useful insights to predict the trends and empower the next level of customer-focused product solutions or platforms.

Among many, NLP is being utilized for programs that require to apply techniques like:

  • Machine translation: Using different methods for processing like statistical, or rule-based, with this technique, one natural language is converted into another without impacting its fluency and meaning to produce text as result.
  • Parts of speech tagging: NLP technique of NER or named entity recognition is key to establish the relation between words. But before that, the NLP model needs to tag parts of speech or POS for evaluating the context. There are multiple methods of POS tagging like probabilistic or lexical.
  • Information grouping: An NLP model which requires to classify documents on the basis of language, subject, type of document, time or author would require labeled data for text classification.
  • Named entity recognition: NER is primarily used for identifying and categorizing text on the basis of name, time, location,…

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