As a data science engineer , I found one of the most challenging task is Text Analytics. Natural language processing is one the cutting edge technology to talk with computers . I think ! when I mention talking with 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 . Its seems a magic when computer replies back . When your system understand the context , Its really become the 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.
Top 5 most important NLP Tasks-
Lemmatisation process in NLP is used to convert any word into lemma or dictionary form . In the different side the Stemming does the quite similar task but not exactly same . Stemming reduce the word into its lemma (root ) form . Some time the root form does not have significance meaning in 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 its 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 piece of text .
This nlp task tags the part of speech correspond to words . In simple word , You can tag and identify words by their type in Part of speech like noun , pronoun , verb , adverb etc .
NER ( Name Entity Recognization ) is for identifying the type of entity . I mean it helps to identify whether a word is name of a person , organization etc . In the different side Named Entity Disambiguation is something where we identify entities for example –
“Apple release a new mac version yesterday ” . Here Apple is a company not a fruits .
5 . Others –
It really happens to me a lot . While designing the articles specially when you have so many stuffs to cover in top 5 bucket . At the last bucket I have three more to cover .
5.1 Text Summarization –
Using NLP , You can create summary of large text . You can easily imagine the use cases for this .So many mobile application which are 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 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 feed backs 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 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 next advance stage . There could be multiple classes in which sentiment classification done . Like mostly people does 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 the time in support team it happens they recieve some response from user they forward it the the person who is comfortable on that language . We can automated this manual classification using this NLP task .
you do not need to do these NLP task from scratch . There are already so many NLP Libraries which can perform these complex action in just a function call .Actually these API are already trained ML model on big corpuses on different languages . Anyways at the end , I would like to take your feed back on this article – Top 5 most important NLP Tasks : Never miss . Please suggest your modification on the top of this article to us .
Data Science Learner Team
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