The Importance of NLP
Artificial Intelligence experts are constantly at work to come up with machines that perfectly replicate complicated tasks that only the human mind could achieve in the past. One of the most significant tasks that human minds are capable of is the ability to create and understand complex languages. Languages are one of main pillars upon which humanity has made so much progress. Hence, language is one of the most discussed concepts for AI professionals. Over the past two decades, rapid progress has been recorded in the field of Natural Language Processing (NLP).
What is NLP?
NLP is the process in which machines decode human languages. Simply put, it is the road that links human to machine understanding. Using these methods, machines are able to generate natural machine-to-human languages. The benefits of computer programs that can decode complex linguistic patterns are countless. Discussed below are the key techniques NLP experts use to implement this valuable tactic into our day to day activities.
Named Entity Recognition
Name Entity Recognition (NER) is the most primitive algorithm in the field of NLP. The process extracts the core ‘entities’ present in the text. These entities represent the fundamental themes in the text. Entities could be the names of people, names of companies, dates, monetary values, quantities, time expressions, medical codes, locations, and other key information found in the text.
This text extraction method focuses on identifying and categorizing entities into pre-classified groups. For example, consider this sentence –
“The temperature in Garden City, Kansas is close to 100-degrees on this sunny May Day”.
In this piece of information, the NER algorithm would categorize –
“Garden City, Kansas” as “Location”
“100-degrees” as “Temperature”
“May Day” as “Date”
NER is based on basic rules of grammar. There are several simple and complex models that companies use to manage large data sets.
Sentiment analysis is an NLP tool/algorithm that interprets and classifies the emotions mentioned in the text. The way of assigning emotions can be as simple as having three pre-classified groups - good, bad, or neutral. Or, the text data can be subjected to more complex NLP techniques.
Sentiment analysis follows a fairly straightforward principle. The essential steps that such algorithms take include -
Breaking down each piece of information into core elements (sentences, parts of speech, tokens, etc.)
Classifying each sentiment-carrying element
Assigning each element with a sentiment score
Coalescing the scores to get several layers of detailed analyses
This example easily explains this underlying principle–
Let’s examine these two newspaper reports -
i. Both the teams were dreadful. Fans were bored throughout the game.
ii. Both teams played well but they should learn how to take their chances.
Both sentences discuss similar subjects – a report regarding a sports event. It is apparent that the first sentence is much more negative. But, can a machine detect these ‘sentiments’. A Sentiment Analysis algorithm will treat as sentiment-carrying elements in the two sentences above as -
Teams were dreadful | Fans were bored |
Both teams played well | they should learn how to take their chances |
The Sentiment Analysis algorithm will assign scores to every element to come up with a final score. Companies assess their customer reviews using Sentiment Analysis Programs. Since they cannot manually read every comment/review, these programs help them realize whether or not their service is being appreciated by the customers.
Text Summarization is a field of NLP that deals with techniques of summarizing massive sets of textual data. It is mainly used by experts to assess information present in news or research articles.
The two key techniques in Text Summarization are extraction and abstraction. Extraction is a process that assesses large amounts of textual data to ‘extract’ short and definitive summaries. Abstraction programs create summaries by creating new text based on the assessment of the original source text.
Aspect mining classifies the different features or elements in the text. Typically, it is combined with sentiment analysis programs and used by companies to detect the nature of their customer responses. When those aspects and sentiments are combined, companies can get a clear idea about various aspects of customer information. Using these tools, large amounts of text data can be condensed into small sentences such as -
Customer service – can be better
Communications – Positive
Pricing– not satisfactory
Topic modeling is a complex NLP tool used to classify natural topics present in textual data. These techniques do not require any form of human supervision. Some commonly used Topic Modeling algorithms include -
Correlated Topic Modeling
Latent Dirichlet Allocation
Latent Semantic Analysis (LSA)
Lastly and most importantly Machine Translation is a vital NLP tool. The techniques that fall under the bracket of Machine Translation are used to both analyze and generate language. Top companies employ complicated machine translation systems. They play a vital role in modern commerce. These tools have been able to break language barriers globally, enabling people around the world to access foreign websites and interact with users who speak foreign languages. Last year, the Machine Translation industry hit the $40 Billion revenue mark. Here’s how MT helps companies:
Google Translate processes over 100 billion words every day.
Facebook uses MT to enable automatic post/comment translation.
MT enables eBay to process cross-border business, connecting customers and sellers on a global scale.
Microsoft is pioneering AI-powered machine translations, helping Android and iOS users to get access to easy translation.
Neural Machine Translation (NMT) is a vital subset of MT. In neural approaches, machine translation programs employ artificial neural networks to predict the probability through word sequencing, modeling complicated sentences into single integrated models.
Overall, NLP is still at a primitive stage. There are thousands of vital language related details and complications that need to be addressed. However, with heavy investments in correlating fields such as human feature engineering, experts are expecting to tackle independent machine learning difficulties at an exponential rate. These complicated systems are set to make our worlds much less complicated.
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