Natural Language Processing: Tasks and Application Areas

As companies grasp unstructured data’s value and AI-based solutions to monetize it, the natural language processing market, as a subfield of AI, continues to grow rapidly. With a promising $43 billion by 2025, the technology is worth attention and investment. How can businesses leverage NLP? What are the main areas of natural language processing applications? Having first-hand experience in utilizing NLP for the healthcare field, Avenga can share its insight on the topic.

How companies use NLP

The amount and availability of unstructured data are growing exponentially, revealing its value in processing, analyzing and potential for decision-making among businesses. NLP is a perfect tool to approach the volumes of precious data stored in tweets, blogs, images, videos and social media profiles. So, basically, any business that can see value in data analysis – from a short text to multiple documents that must be summarized – will find NLP useful.

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.

→ Read how NLP social graph technique helps to assess patient databases can help clinical research organizations succeed with clinical trial analysis.

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.

→ Read our article on Pharma Manufacturing – Improving the risk-reward calculus for clinical trials: How natural language processing and machine learning can boost success in drug development by Michael DePalma and Igor Kryglyak

Challenges blocking NLP from mass adoption

Despite years of research and more advanced AI, natural language processing is still not easy. Hundreds of languages with their own syntax rules are just the tip of the iceberg. Every application area has issues that make NLP models imperfect and needing improvement. In addition to the challenges we mentioned earlier, here are some of the most significant reasons NLP is not yet mainstream:

• 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.

→ Discover the sentiment analysis algorithm built from the ground up by our data science team.

Avenga’s nlp expertise in healthcare

Clinical research organizations can benefit greatly from deploying AI-powered systems for clinical trials. They help overcome medical research stagnation by enrolling a sufficient number of relevant patients for credible trial results, which is a huge advantage these days.

Natural language processing helps Avenga’s clients – healthcare providers, medical research institutions and CROs – gain insight while uncovering potential value in their data stores. By applying NLP features, they simplify their process of finding the influencers needed for research — doctors who can source large numbers of eligible patients and persuade them to partake in trials.

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, download Avenga’s whitepaper on the use of NLP for clinical trials.