As a data science engineer, I found one of the most challenging tasks is Text Analytics. Natural language processing is one of the cutting edge technology to talk with computers. I think! when I mention talking with the computer , you are surprised, Right? I am not kidding. It is possible to talk with computers if you know NLP. Chatbots are capturing every market /Industry as never anything did. It seems magic when the computer replies back. When your system understands the context, It really becomes extreme in customer satisfaction. All these magic happens on the top of small NLP tasks. In this article, we are going to cover those most common and Top 10 most important NLP Tasks.[toc]
Top 5 most important NLP Tasks-
The lemmatization process in NLP is used to convert any word into lemma or dictionary form. On the different side the Stemming does quite a similar task but not exactly the same . Stemming reduces the word into its lemma (root ) form. Sometimes the root form does not have significant meaning in the dictionary . For example –
beautiful —stemming —-> beauti
beautiful —lemmatisation —->beautiful
This Technique in NLP is used to covert the word and phrases into vector form. As you know computer can not process over text directly. So it converts them into vectors. This is the base technique or algorithm in information extraction from a text piece. Here Semantic Text Similarity is the process to identify the similarity between a piece of text.
This NLP task tags the part of speech corresponds to words. In simple words, You can tag and identify words by their type in Part of speech like noun, pronoun, verb, adverb, etc.
NER ( Named Entity Recognization ) is for identifying the type of entity. I mean it helps to identify whether a word is the name of a person, organization, etc. In the different side Named Entity Disambiguation is something where we identify entities for example –
“Apple releases a new mac version yesterday “. Here Apple is a company not a fruit.
5. Others –
It really happens to me a lot. While designing the articles specially when you have so much stuff to cover in the top 5 buckets. In the last bucket, I have three more to cover.
5.1 Text Summarization –
Using NLP, You can create a summary of large text. You can easily imagine the use cases for this. So many mobile application which is growing in the market are just using this feature for example – Most of the time we do not have so much time to read the complete news article. You can use the text Summarization NLP feature and find out the summary only. Still, it is the subject of research in NLP. Basically all research is going to make it more accurate.
5.2 Sentiment Analysis-
It is one of the important NLP Tasks because all improvements are made on the top of customer feedbacks Right? Rather than classifying them manually using this NLP, we can automate this process. Sentiment Analysis involves so many basic NLP sub-tasks. It also includes a few of the above mention most important NLP Tasks like stemmer etc. If you can classify the sentiment of a text, You can imagine the support automation at the next advance stage. There could be multiple classes in which sentiment classification is done. Like most people do this operation in three classes -Positive sentiment, neutral and negative sentiment, etc.
5.3 Language Identification –
We can use NLP to identify the language of the given text. Most of the time in support team it happens they receive some response from the user they forward it to the person who is comfortable with that language. We can automate this manual classification using this NLP task.
you do not need to do these NLP tasks from scratch. There are already so many NLP Libraries which can perform these complex action in just a function call. Actually these APIs are already trained ML models on big corpus on different languages. Anyways, in the end, I would like to take your feedback on this article – Top 5 most important NLP Tasks: Never miss. Please suggest your modification at the top of this article to us.
Data Science Learner Team
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