Natural Language Processing (NLP) is part of everyday life and it is essential to our lives at home and at work. Without giving it much thought, we send voice commands to our virtual home assistants, our smartphones, and even our vehicles. Voice-enabled applications such as Alexa, Siri, and Google Assistant use NLP and Machine Learning (ML) to answer our questions, add activities to our calendars and call the contacts that we state in our voice commands. NLP is not only making our lives easier, but revolutionizing the way we work, live, and play.
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Differences between Natural Language Processing and Machine Learning
Although Natural Language Processing, Machine Learning, and Artificial Intelligence are sometimes used interchangeably, they have different definitions. AI is an umbrella term for machines that can simulate human intelligence, while NLP and ML are both subsets of AI.
Artificial Intelligence is a part of the greater field of Computer Science that enables computers to solve problems previously handled by biological systems. AI has many applications in today's society. NLP and ML are both parts of AI.
Natural Language Processing is a form of AI that gives machines the ability to not just read, but to understand and interpret human language. With NLP, machines can make sense of written or spoken text and perform tasks including speech recognition, sentiment analysis, and automatic text summarization.
Machine Learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning can be used to help solve AI problems and to improve NLP by automating processes and delivering accurate responses.
As we can see in Figure 1, NLP and ML are part of AI and both subsets share techniques, algorithms, and knowledge.
How Natural Language Processing can be applied
Some NLP-based solutions include translation, speech recognition, sentiment analysis, question/answer systems, chatbots, automatic text summarization, market intelligence, automatic text classification, and automatic grammar checking. These technologies help organizations to analyze data, discover insights, automate time-consuming processes and/or gain competitive advantages.
Translating languages is more complex than a simple word-to-word replacement method. Since each language has grammar rules, the challenge of translating a text is to do so without changing its meaning and style. Since computers do not understand grammar, they need a process in which they can deconstruct a sentence, then reconstruct it in another language in a way that makes sense.
Google Translate is one of the most well-known online translation tools. Google Translate once used Phrase-Based Machine Translation (PBMT), which looks for similar phrases between different languages. At present, Google uses Google Neural Machine Translation (GNMT) instead, which uses ML with NLP to look for patterns in languages.
Speech recognition is a machine’s ability to identify and interpret phrases and words from spoken language and convert them into a machine-readable format. It uses NLP to allow computers to simulate human interaction, and ML to respond in a way that mimics human responses.
Google Now, Alexa, and Siri are some of the most popular examples of speech recognition. Simply by saying ‘call Jane’, a mobile device recognizes what that command means and will now make a call to the contact saved as Jane.
Sentiment analysis uses NLP and ML to interpret and analyze emotions in subjective data like news articles and tweets. Positive, negative, and neutral opinions can be identified to determine a customer’s sentiment towards a brand, product, or service. Sentiment analysis is used to gauge public opinion, monitor brand reputation, and better understand customer experiences.
The stock market is a sensitive field that can be heavily influenced by human emotion. Negative sentiment can lead stock prices to drop, while positive sentiment may trigger people to buy more of the company’s stock, causing stock prices to increase.
Chatbots are programs used to provide automated answers to common customer queries. They have pattern recognition systems with heuristic responses, which are used to hold conversations with humans. Initially, chatbots were used to answer basic questions to alleviate heavy volume call centers and offer quick customer support services.
But AI-powered chatbots are designed to handle more complicated requests making conversational experiences increasingly intuitive. Chatbots in healthcare, for example, can collect intake data, help patients assess their symptoms, and determine next steps. These chatbots can set up appointments with the right doctor and even recommend treatments.
Question-answer systems are intelligent systems that are used to provide answers to customer queries. Other than chatbots, question-answer systems have a huge array of knowledge and good language understanding rather than canned answers. They can answer questions like “When was Abraham Lincoln assassinated?” or “How do I get to the airport?” and can be created to deal with textual data, audio, images and videos.
Question-answer systems can be found in social media chats and tools such as Siri and IBM’s Watson. In 2011, IBM’ Watson computer competed on Jeopardy, a game show during which answers are given first, and the contestants supply the questions. The computer competed against the show’s two biggest all-time champions and astounded the tech industry when it won first place.
Automatic text summarization
Automatic text summarization is the task of condensing a piece of text to a shorter version, by extracting its main ideas and preserving the meaning of content. This application of NLP is used in news headlines, result snippets in web search, and bulletins of market reports.
Market Intelligence is the gathering of valuable insights surrounding trends, consumers, products and competitors to extract actionable information that can be used for strategic decision-making. Market Intelligence can analyze topics, sentiment, keywords, and intent in unstructured data and is less time consuming than traditional desk research.
Using Market Intelligence, organizations can pick up on search queries and add contextually relevant synonyms to search results. It can also help organizations decide which products or services to discontinue or what customers to target.
Automatic text classification
Automatic text classification is another fundamental solution of NLP. It is the process of assigning tags to text according to its content and semantics which allows for rapid, easy retrieval of information in the search phase. This NLP application can differentiate spam from non-spam based on its content.
Automatic grammar checking
Automatic grammar checking, the task of detecting and correcting grammatical errors and spelling mistakes in text depending on context, is another major part of NLP. Automatic Grammar Checking will alert you to a possible error by underlining the word in red.
Advantages and Disadvantages of Natural Language Processing
Like many other forms of Artificial Intelligence, the use of Natural Language Processing comes with advantages as well as disadvantages.
Advantages of NLP include:
- Once implemented, using NLP is less expensive and more time-efficient than employing a person.
- NLP can also help businesses offer faster customer service response times. No matter the time of day or day of the week, customers receive immediate answers to their questions.
- Pre-trained machine learning models are widely available for developers to facilitate different applications of NLP, making them easy to implement.
Advances in NLP are promising, but there are some disadvantages to NLP as well.
Disadvantages of NLP include:
- Training can be time-consuming. If a new model needs to be developed without the use of a pre-trained model, it can take weeks before achieving a high level of performance.
- Another disadvantage of NLP is that ML is not 100 percent reliable. There is always a possibility of errors in predictions and results that need to be taken into account.
Adding NLP and ML to your Product
The number one reason to add Natural Language Processing and Machine Learning to your software product is to gain a competitive advantage. Your users can receive an immediate and 24/7 response to customer service queries with chatbots.
The speed at what risk assessment can be done with ML algorithms can add value to your enterprise.
To learn more talk to an Encora expert.
Natural Language Processing is the practice of teaching machines to understand and interpret conversational inputs from humans. NLP based on Machine Learning can be used to establish communication channels between humans and machines. Although continuously evolving, NLP has already proven useful in multiple fields. The different implementations of NLP can help businesses and individuals save time, improve efficiency and increase customer satisfaction.
- Natural Language Processing (NLP) is a form of Artificial Intelligence that gives machines the ability to read and interpret human language. With NLP, machines can make sense of written or spoken text.
- NLP is constantly evolving, but existing NLP-based solutions include translation, speech recognition, sentiment analysis, question/answer systems, automatic text summarization, chatbots, market intelligence, automatic text classification, and automatic grammar checking.
- The use of Natural Language Processing comes with advantages as well as disadvantages. Businesses can save costs, reduce client wait times, and increase customer satisfaction when implementing NLP. But training can take time and ML is never 100% reliable.
Feel free to contact us to learn more about how NLP can help your business grow.
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