Tasks and applications of natural language processing (NLP)

Natural language processing (NLP) enables computers to understand, analyze, and generate human language. Businesses use NLP for tasks such as sentiment analysis, machine translation, text summarization, information extraction, search, and conversational AI across industries including healthcare, finance, retail, and marketing.

As organizations increasingly look to unlock value from unstructured data, NLP has become one of the fastest-growing areas of artificial intelligence. Grand View Research estimates the global NLP market will grow from $59.7 billion in 2024 to nearly $440 billion by 2030, reflecting rapid enterprise adoption across industries.

How can businesses leverage NLP? What are its most valuable applications? Drawing on Avenga’s experience delivering NLP solutions for healthcare organizations, this article explores the technology’s core tasks, real-world use cases, and current challenges.

How companies use NLP

Businesses use natural language processing (NLP) to analyze unstructured data and turn it into actionable insights for decision-making. As the volume of unstructured data continues to grow, NLP enables organizations to process information stored in tweets, blogs, images, videos, social media profiles, and other sources. Any business that relies on data analysis—from interpreting short text messages to summarizing large document collections—can benefit from NLP.

Advanced systems often include both NLP and machine learning algorithms, which increase the number of tasks these AI systems can fulfill. In this case, they unpuzzle human language by tagging it, analyzing it, performing specific actions based on the results, etc. Think of Siri or Alexa, for example. They are AI-based assistants who interpret human speech with NLP algorithms and voice recognition, then react based on the previous experience they received via ML algorithms.

To dive a bit deeper, the role of machine learning for natural language processing and text analytics lies in improving NLP features and turning unstructured text into valuable insights. So, a common approach looks like this: you train a model to perform a task, then verify the model is correct and apply it to the problem. Here are the main tasks fulfilled with the help of NLP.

Key application areas of NLP

• Searching. NLP algorithms identify specific elements in the text. You can search for keywords in a document, run a contextual search for synonyms, detect misspelled words or similar entries and more.
• Machine translation. This typically involves translating one natural language into another, preserving the meaning and producing fluent text as a result. Different methods and approaches are used here: rule-based, statistical and neural machine translation.
• Summarization. NLP algorithms can be used to create a shortened version of an article, document, number of entries, etc., with main points and key ideas included. There are two general approaches: abstractive and extractive summarization. In the first case, the NLP model creates an entirely new summary in terms of phrases and sentences used in the analyzed text. In the second case, the model extracts phrases and sentences from the existing text and groups them into a summary.
• Named-Entity Recognition. NER is an entity extraction, identification and categorization. It involves extracting names of locations, people and things from the text and placing them under certain categories – Person, Company, Time, Location, etc. The use cases may include content classification for SEO, customer support, patient lab reports analysis, academic research and others.
• Parts-of-Speech tagging (POS). To build NERs and extract relations between words, the NLP model first needs to tag POS: it groups words from the text according to parts of speech based on word definition and context. POS tagging methods and models include lexical-based, rule-based, probabilistic methods, as well as the use of recurrent neural networks and more.
• Information retrieval. With the help of NLP, we can find the needed piece among unstructured data. An information retrieval system indexes a collection of documents, analyzes the user’s query, then compares each document’s description with the query and presents the relevant results.
• Information grouping. Grouping, or text classification, is performed via the text tags. The NLP model is trained to classify documents according to specific attributes: subject, document type, time, author, language, etc. Text classification usually requires labeled data. Information grouping is used for supervised machine learning, which correspondingly triggers a multitude of use cases.
• Sentiment analysis. It’s a type of text classification where the NLP algorithms determine the text’s positive, negative, or neutral connotation. Use cases include analyzing customers’ feedback, detecting trends, conducting market research, etc., via an analysis of tweets, posts, reviews and other reactions. Sentiment analysis can encompass everything from the release of a new game on the App Store to political speeches and regulation changes.
• Answering queries. An automated question answering system applies a set of NLP techniques to analyze unstructured documents – from Wikipedia articles to social media newsfeed or medical records – by retrieving the needed information piece, analyzing it and using the best part to answer the question.
• Automated speech recognition (ASR). NLP techniques are actually designed for text but can also be applied to spoken input. ASR transcribes oral data into a stream of words. Neural networks and hidden Markov models are used to reduce speech recognition’s error rate, however, it’s still far from perfect. The main challenge is the lack of segmentation in oral documents. And while human listeners can easily segment spoken input, the automatic speech recognizer provides unannotated output.

The value of using NLP techniques is apparent, and the application areas for natural language processing are numerous. But so are the challenges data scientists, ML experts and researchers are facing to make NLP results resemble human output.

Challenges blocking NLP from mass adoption

Natural language processing has advanced significantly, but accurately understanding and generating human language remains a complex challenge. Differences in grammar, syntax, context, and writing styles across hundreds of languages make it difficult to achieve consistently human-like results. In addition to the challenges mentioned earlier, here are some of the key obstacles that continue to limit broader NLP adoption:

• Encoding schemes: the text is encoded using ASCII, UTF-8, UTF-16, or Latin-1 schemes, which differ slightly in assigning new characters. Punctuation and numbers may need specific processing from NLP. Also, you must pay attention to emojis, hyperlinks, extensions, specific symbols in usernames and so on.
• Tokenization in some languages: NLP models tokenize text, i.e., break it into a sequence of words (or tokens). However, in languages like Chinese, unique symbols are used for words and sometimes phrases, so the tokenization process doesn’t work the same as with the delineated words.
• Generating dependency graphs: machines must determine every word’s position in a sentence based both on POS tagging and context. For this to happen, dependency graphs have to be built, but the process is quite difficult. One word can belong to different parts of speech, or one POS can have different places in the sentence or text.
• Understanding context: one of the most complex tasks for AI. Understanding and deriving the context requires custom-made knowledge graphs for NLP systems. They also have to be domain-specific, so the probabilistic approach must be improved here. Understanding vocabulary terms’ semantics within the context is another issue for the machine to solve.

Despite these difficulties, NLP is able to perform tasks reasonably well in most situations and provide added value to many problem domains. While it is not independent enough to provide a human-like experience, it can significantly improve certain tasks’ performance when cooperating with humans. Avenga’s experience proves this statement.

Avenga’s NLP expertise in healthcare

Avenga helps healthcare providers, medical research institutions, and contract research organizations (CROs) use natural language processing to uncover insights from clinical and research data, streamline research processes, and support more efficient clinical trials.

Clinical research organizations can benefit greatly from AI-powered systems that help identify and enroll eligible patients, making it easier to conduct credible clinical trials and address one of medical research’s long-standing challenges. By applying NLP, Avenga helps clients identify healthcare professionals who can source eligible patients and encourage participation in clinical research.

NLP and ML help optimize and simplify daily operations, provide more value to patients and enable efficient and rewarding work for personnel.

Avenga’s NLP services include:

• Named entity recognition
• Topic clustering
• Keyphrase extraction
• Multi-document summarization
• Relationship extraction and more

Explore how technology can equip and complement biotech and pharma companies seeking facilities to run their clinical trials with the utmost efficiency. If you decide to develop a solution that uses NLP in healthcare, we will be here to help you.

Conclusion

Natural language processing can bring value to any business wanting to leverage unstructured data. The applications triggered by NLP models include sentiment analysis, summarization, machine translation, query answering and many more. While NLP is not yet independent enough to provide human-like experiences, the solutions that use NLP and ML techniques applied by humans significantly improve business processes and decision-making.

To find out how specific industries leverage NLP with the help of a reliable tech vendor, contact our Avenga experts.

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Igor Kruglyak

Sr Advisor to the CEO at Avenga