The Natural Language Processing (NLP for short) in the field of study that focuses on understanding by the computer of human language. NLP is the ability to train computers to understand both written and human language. NLP techniques are necessary to grasp the meaning of an unstructured text of user documents or communications. Therefore, NLP is the main way in which systems can interpret the text and the spoken language. NLP is also one of the basic technologies that allow non-technical individuals to interact with advanced technologies. For example, instead of having to code, NLP can help users ask questions about the system with complex data sets. Unlike structured database information that relies solely on schemas to add context and meaning to data, unstructured information must be analyzed and labeled to find the meaning of the text. NLP covers part of Data Science, Artificial Intelligence (Machine Learning), and linguistics.
In NLP, computers analyze human language, interpret it, and give meaning so that it can be used practically. Using NLP, we can do tasks such as automatic text summary, language translation, relationship extraction, sentiment analysis, speech recognition, and classification of articles by subject.
The Great Challenge
NLP is considered one of the great challenges of artificial intelligence since it is one of the most complicated and challenging tasks: how to really understand the meaning of a text? How to intuit irony, jokes, or poetry? If the strategy/ algorithm we use does not overcome these difficulties, the results obtained will not help us.
In NLP, it is not enough to understand mere words; you must understand the set of words that make up a sentence and the set of lines that comprise a paragraph — giving a global sense to the analysis of the text/ speech to be able to draw good conclusions.
Our language is full of ambiguities of words with different meanings, turns, and different meanings depending on the context. This makes the NLP one of the most difficult tasks to master.
NLP Applications
NLP is everywhere even though we do not realize it. Consider the case where you attempt to send an email without the subject. It will prompt you to make corrections. This is natural language processing at work. Even though the use of NLP has been implemented in some areas, its implementation is expected to rise. Here are the areas in which NLP is currently applied:
Customer Service
Currently, there are many virtual assistance solutions that rely on the use of NLP. In these applications, the first request from the customer is handled by the AI. A good example is when a bank uses an automated system to answer customer queries or help them to know the best type of bank account they should go for. When the customer’s queries become complex, the application will redirect to a helpline or the right landing page. Nina is an example of such an application. Most banks have implemented it in their systems for customer support.
Market Intelligence
Most business markets are much impacted and influenced by market knowledge and information exchange amongst various companies, stakeholders, regulatory bodies, and governments. Every business should stay updated with the current trends and changes in market standards. NLP is a great tool for monitoring market intelligence reports to extract new information that can help businesses to come up with new strategies.
When NLP is used in financial marketing, NLP can give great insights into the market status and employment changes, tender delays, closings, and extracting information from repositories.
Management of Advertisement Funnel
You should know who your customer is, what they need, and where they are located. NLP is a great tool to help you make sure that you do intelligent targeting when running your business ads. It will help you create the right ads, direct them to the right audience, the right place, and at the right time. NLP has the feature of keyword matching that can help you to do this accurately.
Sentiment Analysis
NLP is one of the best tools through which businesses can analyze the feedback they get from their followers for the messages they publish on their social media platforms. With NLP, the emotion and attitude of the writer can be analyzed easily. The business can know the mood of the customers regarding their brand. This way, they can make any necessary changes to their products or services.
Such information is very important to make any necessary improvements. | They can also design a better customer experience.
With natural language processing, a computer can understand the human language while being spoken. This has led to the increased popularity of natural language processing, not forgetting the availability of big data, growing interest in machine-human communications and the discovery of new computing algorithms. With natural language processing, an intelligent system such as a robot can perform according to our instructions issued in a plain language such as English. NLP has been applied in various fields such as customer service, chatbots, market intelligence, managing advertisements, and sentiment analysis.
We live in a world in which we humans surely differentiate ourselves from other species by having developed skills such as language. We constantly communicate, speaking, with words and gestures. We are surrounded by symbols, posters, ads, etc. The NLP is a fundamental tool that we must learn and master to be able to train our machines and make them much more versatile when interacting with the environment, giving them the ability to understand better, to explain themselves: to communicate. We must be able to understand the various tools and techniques used in NLP and know how to use them to solve the appropriate problem. The NLP covers a lot and is a journey that begins but never ends; new papers and new instruments of action continue to appear. By combining these “traditional” NLP techniques with Deep Learning, the combination of new possibilities is exponential!
How is the Computer Able to Understand the Language?
We will have to assemble different models with the language, create structures, and, with them, feed Machine Learning algorithms.
We can start, for example, by taking an extensive text. We will use Regular Expressions to subdivide the text into words. We can count the words, their frequency. If there is any pattern, for example, if always after a word X, a word Y always comes. We can analyze how the end of the words, for example, “verbs ending in” ar, er, go “and discover the root of the word. We could group words with similar meanings as opposed to their antonym words.
In short, we can process language components in various ways, including grammar and synta. We can try to create support structures that will serve as inputs to apply Linear Regression, Logistic Regression, Naïve Bayes, Decision Tree, and Neural Networks as the result we are looking for.
Common Techniques Used in NLP
Tokenize: separate words from the text in entities called tokens. We must think about whether we will use punctuation marks as a token, whether we will give importance to capital letters or not, and if we unify similar words in the same token.
Tagging Part of Speech (PoS): Classify sentences in a verb, noun, adjective preposition, etc.
Shallow parsing/ Chunks: Serves to understand the grammar in sentences. The tokens are parsed, and a structure tree is built from their PoS.
Meaning of the words: lexical semantics and word sense disambiguation.
Pragmatic Analysis: detect how things are said: irony, sarcasm, intentionality, etc.
Bag of words: it is a way of representing the vocabulary that we will use in our model and consists of creating a matrix in which each column is a token and the number of times that token appears in each sentence will be counted (represented in each row).
word2vec: It is a technique that learns to read huge amounts of texts and memorize what words seem to be similar in different contexts. After training enough data, 300-dimensional vectors are generated for each word forming a new vocabulary where the “similar” words are located close to each other. Using pre-trained vectors, we managed to have a wealth of information to understand the semantic meaning of the texts.
Conclusion
Natural Language Processing is a branch of artificial intelligence that deals with machines such as computers interpreting natural languages such as English. This means that NLP has to deal with computers interacting with human languages.
Today, companies are generating too much data. This data is normally stored in the form of text. Due to the huge volume of the data, it may be hard for any business to process it manually. Thanks to natural language processing as it’s the best tool to process such data. When processed, this data can give much knowledge which is of much importance to the business. Businesses can learn the passions and interests of their customers. This can help them adjust their plans appropriately to meet the demands of users.
