Some of the most important and useful NLP tasks.

Some of the most important and useful NLP tasks.

In this article, I will let you know some of the NLP tasks which were performed and later we will deploy on to the web to make it a complete package.

Natural Language Processing(NLP)

Natural Language Processing, usually called as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages. NLP plays a critical role in supporting machine-human interactions.

In this article, I will let you know some of the NLP tasks which were performed and later we will deploy on to the web to make it a complete package.

The tasks are mentioned below.

  1. Analyzing the text and getting the tokens and lemma of the text.
  2. Also getting the NER(Named Entity Recognition) from the text entered.
  3. Sentimental Analysis.
  4. Text Summarization (Extract Summarization)
  5. Machine Translation.

I will throw some light on each and every task mentioned above as we proceed further.

1. Tokens and Lemma

A token is the smallest part of a corpus. And tokenization is the task of chopping it up into pieces, called tokens.

For example:

Input: NLP and Machine learning go hand in hand.

After Tokenization, the output is nothing but each of the word present in this sentence. NLP is one token Machine is another token and this list goes on like this.

Lemma is like getting to a root of that given word. Lemma uses wordnet corpus. It can be used when we want more human understandable words, as the output of lemmatization is a proper word. It will be more clear with an example.

Lets take three words “going”, “goes”, “gone”. The lemma is nothing but getting the root word which is “go”.

text-summarization sentiment-analysis nlp machine-learning

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