Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand and generate human language. This technology leverages computational linguistics and machine learning to analyse text and speech, allowing for effective human-computer interactions
Since the concept was first introduced, a natural barrier to the development of effective artificial intelligence has been language. Simply put, machines do not communicate in the same way that living beings do. The ambiguity, nuances and layers of connotation in human language lack the rigid structure and clear definition that machines rely on. This is one of the reasons that computer programming incorporates special coding languages—if you want to tell a computer what to do, you have to talk to it in a very specific way. Or rather, that was the case, until the development of NLP.
NLP stands for natural language processing and is designed to bridge human communication and machine understanding. NLP allows computer systems to interpret natural language in much the same way that humans do, making it foundational technology in the growing field of artificial intelligence.
The origin of NLP dates back to the early 20th century, with some of the earliest ideas in linguistics laid by Ferdinand de Saussure. Advances continued with the work of Alan Turing in the 1950s, which paved the way for today's sophisticated NLP systems. As computational power and AI techniques evolved, NLP grew into a field of its own, characterised by its ability to interpret and recreate complex human language—nuances and all.
The ideal result of natural language processing is to make it possible for people and machines to communicate as easily as individuals converse with one another. The steps needed to get there require many smaller tasks—ranging from parsing and understanding text to generating human-like responses. Each of the following task addresses a specific aspect of language, making it possible for machines to handle even some of the most complex human language interactions:
Sentiment analysis is a technique used to detect the emotional tone behind words. It is particularly valuable in social media monitoring, marketing and customer service as it helps organisations understand consumer opinions and the (often unspoken) feelings behind responses. By analysing text data, businesses can gauge public sentiment towards products, brands or services and adjust their strategies accordingly.
Capable of converting spoken language into text, speech recognition is fundamental to creating responsive voice-activated systems like digital assistants and interactive voice response (IVR) systems. This task involves analysing the sound waves of speech and using algorithms to interpret the content and convert it into written form, making it accessible for further processing.
Grammatical tagging (also called part-of-speech tagging) involves labelling each word in a sentence according to its grammatical role—noun, verb, adjective etc. This helps the system in understanding the structure of sentences and improves the accuracy of linguistic analysis. Tagging is crucial for various language processing applications that require a deep understanding of grammar, such as text correction tools and advanced content analysis systems.
Named entity recognition (NER) identifies and classifies key elements in text into predefined categories such as the names of people, organisations, locations, expressions of times, quantities, monetary values and others. NER is used in content recommendation systems, information retrieval from large databases, and automated customer support systems to extract relevant details from unstructured data.
Co-reference resolution is responsible for finding all expressions that refer to the same entity within a text, making it possible to build a complete and unambiguous understanding of what is being discussed. This is essential for activities such as document summarisation, question answering and maintaining context in dialogue systems.
Natural language generation transforms structured data into natural language in such applications as report generation, automated content creation, and real-time translation of data into readable text. This task mirrors the process of human language production, giving machines the capacity to create responses that are often indistinguishable from those made by humans.
While the basic concept of machines understanding and generating natural language might seem relatively simple, it opens a limitless array of possibilities in various fields. Here are some of the more significant use cases for NLP:
- Machine translation
NLP is not restricted to any single language; machine translation allows digital systems to translate text or speech from one language to another automatically, facilitating communication in global markets and among diverse populations. - Virtual agents and chatbots
These programs simulate conversation with human users to provide customer support, gather information or automate tasks. Digital systems capable of interacting directly with users allow for faster issue resolution and increased accessibility. - Customer sentiment analysis
Employing detailed sentiment analysis, this approach takes a deep look at the context and meaning hidden in content from social media, call-centre recordings, valuable insights into customer perceptions and market conditions. - Spam detection
Spam detection identifies and filters out unwanted emails by analysing text patterns and flagging suspicious or unsolicited content—crucial for maintaining email usability and security. - Text summarisation and classification
Text summarisation uses NLP technology to automatically generate condensed versions of lengthy or otherwise-complex documents, making it easier for human readers to quickly understand key points and categorise texts without reading the entire content.
The use cases listed above are only a tiny fraction of what NLP can do. Natural language processing has potential to transform multiple sectors, driving advancements in how businesses operate and engage with their buyers. As companies collect more and more unstructured data from various sources, NLP is playing an ever-greater role in extracting actionable insights and automating essential processes. This is creating opportunities for organisations to enhance their approach to decision making and significantly improve customer experiences.
At the same time, NLP technologies help companies overcome language barriers and extend their reach to customers and markets outside of their immediate geographical region. By automating translation and facilitating multilingual customer support, businesses' expansion need not be limited by fluency.
Taken all together, familiarity with NLP can provide businesses with a competitive edge, enabling them to harness the full potential of their data and improve their service offerings to better meet the expectations of their customers.
The importance of NLP cannot be understated, particularly as AI continues to evolve and integrate into everyday life. But this is not to say that natural language processing is perfect; there are several key considerations and concerns that are worth acknowledging:
Context is king in NLP. The meaning of words and phrases can change based on their usage in a sentence, which requires algorithms to interpret context correctly to properly understand intent and meaning. A solution is to implement context-aware algorithms which go further to consider the surrounding text and help the AI more accurately understand the usage and meaning of words.
Detecting irony and sarcasm in text is challenging enough for humans; it can be almost impossible for machines. This is because it involves nuances that are often understood through tone or context, not just the literal meaning of words. NLP systems must discern these subtleties to interpret sentiments and intents accurately. Advanced sentiment analysis techniques, including deep learning models trained on large, annotated datasets that include examples of irony and sarcasm, can help improve recognition.
Developing and maintaining advanced NLP systems can be expensive. They require substantial investment in data acquisition, computing resources and skilled personnel, which may be prohibitive for some organisations or underfunded researchers. Utilising cloud-based NLP services can reduce costs by providing scalable resources and maintenance. Open-source tools and frameworks also lower the barrier to entry by providing pre-built models backed by supportive and helpful online communities.
NLP systems must handle errors in input data, such as typos in text or mispronunciations in speech, which can complicate understanding and processing. Comprehensive preprocessing steps (like spell-checking, grammar correction and noise reduction in voice inputs) can enhance the accuracy of the data before it is processed by the NLP.
Not everyone communicates in text-book standard language. The informal nature of colloquial speech and slang varies widely by culture and community, presenting challenges for NLPs to stay current and relevant, and to interact with audiences on their terms. Continuously updating language models with current datasets and incorporating social media and other real-time sources of language usage can help systems stay up to date with evolving language trends.
Many NLP models, especially those based on deep learning, do not easily reveal how they arrive at specific conclusions. This can make it difficult for users to understand or trust the system's decision-making processes. This is often referred to as the 'black box' problem. Techniques such as layer-wise relevance propagation (LRP) or shapley additive explanations (SHAP) can offer insights into which inputs most influence the outputs, increasing overall transparency and trustworthiness.
Creating NLP systems that can effectively process multiple languages involves understanding and integrating the syntactic, semantic and pragmatic nuances of each language, which requires extensive data and sophisticated modelling techniques. Multilingual models that can transfer learning across languages may address this issue by leveraging similarities between languages to improve processing efficiency even when interacting in a range of vernaculars.
Despite the challenges inherent in natural language processing, there are also some undeniable benefits—particularly for businesses. Among the most exciting advantages include:
- Analysing large data sets
NLP can quickly process and analyse vast amounts of unstructured text data, which would be impractical for humans to analyse on their own. This capability empowers organisations to uncover valuable insights from customer feedback, social media conversations and other sources. - Streamlining daily processes and enhancing productivity
By automating routine tasks such as data entry, customer support and information retrieval, NLP can streamline daily operations, freeing up human resources for more complex tasks. - Performing objective analysis
NLP provides tools for unbiased and consistent analysis of text data. This objectivity is crucial in fields like market research and legal document review, where human bias can skew results. - Offering actionable insights
From sentiment analysis to trend detection, NLP helps businesses understand underlying patterns and sentiments in text data, providing clear conclusions that can inform decision-making and strategic planning. - Improving customer experience
NLP enhances customer interaction with businesses through chatbots and virtual assistants that can understand and respond to human language in a natural way. This improves engagement while boosting customer satisfaction and loyalty.
Natural language processing involves a series of steps to interpret and generate human language automatically, bringing together various aspects of machine learning, deep learning, computational linguistics and various other computer sciences. Although the process itself is extremely complex, the process typically looks something like the following steps:
- Data collection
Before the NLP can begin parsing and understanding language, it needs to be trained in language. This involves gathering a vast array of unstructured text and speech data from various sources. - Preprocessing
The data is cleaned and organised, a process that includes tokenisation, removal of stop words, stemming and lemmatisation to simplify the text. - Model training
NLP systems employ machine learning and deep learning techniques to train on the preprocessed data to locate patterns and language structures. - Feature extraction
Specific features from the data are identified and extracted. These features are used to create highly detailed datasets for use in prediction, classification and clustering. - Algorithm development
Algorithms are developed and refined to help the system accurately interpret and generate language based on the training data. - Testing and validation
The model's performance is evaluated on new, unseen data to ensure its accuracy and effectiveness. - Deployment
The NLP model is integrated into applications for end-use. - Continuous learning
As new data is made available, the model is updated to improve and adapt the system to evolving linguistic trends.
The way humans and machines communicate is changing, and as the application of natural language processing expands, its importance in business is becoming evident. However, reaping the full benefits of NLP while navigating its complexities often requires partnering with an experienced service provider. ServiceNow, a leader in AI-enhanced automation, provides the solution: The Now Platform®.
Offering comprehensive NLP capabilities integrated within an extensive suite of enterprise automation tools, the Now Platform has everything organisations need to enhance their approach to natural language processing. Employ virtual agents to interact naturally with users in a range of languages. Use predictive intelligence to turn written sentiment into reliable insights. Uncover knowledge gaps and create automated support solutions to reduce call volumes and mean time to resolution (MTTR). Optimise user search results with intelligent recommendations. ServiceNow puts AI and NLP at the centre of your business, streamlining your workflows and enhancing your organisation's capabilities throughout.
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