Natural language Processing has been ranking for a while on top at Upwork, the website that lists the fastest growing skills in the job market globally. The demand for workers with training and experience in NLP (Natural Language Processing) has been increasing at a way quicker pace than any other skill. The demand for NLP professionals grew by over 200% in just the last one year.
So what is Natural Language Processing?
NLP or Natural Language Processing generally refers to the ability of a system to understand human speech as it is spoken. Natural Language Processing is a crucial component of AI (Artificial Intelligence) and depends on Machine Learning, a particular kind of AI which analyzes data and makes use of data patterns in order to improve the understanding of speech by a program.
The use of Natural Language Processing
There are several NLP tasks that are incorporated into software programs these days, they include:
- Deep Analytics: This involves the use of advanced data processing techniques so as to extract specific information from multisource or large data sets. Deep analytics is specifically useful when one deals with properly targeted or highly complex queries with semi-structured and structured data. Deep analytics is used in the scientific community, pharmaceutical field, financial sector and biomedical industries. Increasingly though, deep analysis is also used by companies and organizations that are interested in mining data that have business value from data sets of consumers (usually extremely expansive).
- Parsing, sentence segmentation and part-of-speech tagging: NLP is also used to analyze portions of a sentence in order to better understand the grammatical construction of sentences.
- Entity Extraction: A named entity definition in data mining is a word or a phrase which clearly identifies an item from a whole set of other items which have similar attributes. For instance, geographic locations, first, last names, company names, addresses, phone numbers, company names, etc. Named entity extraction also is called entity recognition that makes mining easier.
- Machine Translation: NLP is being increasingly used for programs in Machine Translation where in any human language can be automatically translated into another human language.
- Automatic Summarization: NLP can be used to produce a lot of readable summary from a lot of text. For instance, one can use automatic summarization to create a short summary of a lengthy academic article.
- Co-reference resolution: In chunk of texts, in order to determine which words/phrases are used to refer to the same objects, co-reference resolution can be used.
The demand for NLP:
- Chatbots for messaging apps are rising: There are over 3 billion people who presently make use of apps like Whatsapp, FB messenger and WeChat. A messenger chatbot allows organizations to engage personally with customers. Over 64% of customers believe that companies need to be contactable on chat applications and incorporating chatbot with effective NLP is crucial.
- NLP is improving customer service: A prediction by Gartner states that over 85% of customer-enterprise relationships will happen without much human interaction by the year 2020. Virtual assistants are all set to become the face of organizations.
- Accruing of a lot more data than ever: The world puts out as much as 2.5 exabytes of data every day which means 90 years of worth high definition videos or 530,000,000 songs or 150,000,000 iPhones. Companies are frantically looking for effective means of mining the gold rush of data. NLP is one of the most used ways.
While NLP is in high demand, it is crucial for individuals and employees to be trained the right way with hands-on experience before they get their hands dirty with the actual project. Find out how you can get help from IIHT for your NLP training needs at els.iiht.com