What is conversational intelligence?

Your customers are talking. Are you listening?

Authenticx generates NLU algorithms specifically for healthcare to share immersive and intelligent insights.

Learn More

Myths vs. Facts: How Artificial Intelligence Is Changing the Way Healthcare Listens


Software that connects qualitative human emotion to quantitative metrics.​

Collecting data is only part of the equation. Understanding begins by listening and engaging with the story your customers are sharing.

See Authenticx in Action

What is NLP

Having access to a plethora of customer data and a large workforce providing customer care solutions, the contact center industry will benefit from using natural language processing (NLP) to effectively listen to the voice of the customers and respond to their needs.

By listening to customer voices, business leaders can understand how their work impacts their customers and enable them to provide better service. Companies may be able to see meaningful changes and transformational opportunities in their industry space by improving customer feedback data collection.

What is natural language processing? By definition, natural language processing is a subset of artificial intelligence that helps computers to read, understand, and infer meaning from human language.

NLP combines linguistics and computer science to extract meaning from human language structure and norms, as well as develop NLP models to break down and categorize important elements in both text and voice data. NLP models can perform tasks such as sentiment analysis, or determining whether data sentiment is positive, negative, or neutral; and speech recognition, or identifying and responding to human speech and transcribing spoken word into a text.

How does natural language processing work? NLP works by collecting, analyzing, and utilizing patterns in stored data. To allow computers to understand grammatical structure, phrase structure rules are used, which are essentially rules of how humans construct sentences.

Phrase structure rules break down a natural language sentence into several parts. Following these rules, a parse tree can be created, which tags every word with a possible part of speech and illustrates how a sentence is constructed. By fragmenting data into smaller chunks and putting them back together, computers can process and respond to information more easily. This process can be repeated with a voice search, in which computers can recognize and process spoken vowels and words, and string them together to form meaning.

To process natural language, machine learning techniques are being employed to automatically learn from existing datasets of human language. NLP technology is now being used in customer service to support agents in assessing customer information during calls.

Not all companies may have the time and resources to manually listen to and analyze customer interactions. Using a software solution such as Authenticx will enable businesses to humanize customer interaction data at scale.

Authenticx uses AI and natural language processing to sift through large volumes of customer interactions and surface what is most important. By using Authenticx, organizations can listen to customer voices and gain valuable insights from customer conversations.

What is NLP techniques

What is NLP techniques? To put it simply, NLP Techniques are used to decode text or voice data and produce a natural language response to what has been said.

Organizations may refer to NLP tasks and NLP techniques list to learn how natural processing language technology processes speech and analyzes text, and how NLP technology can scan and classify emails in order to derive meaning.

One example of common NLP tasks and techniques is text classification, which involves analyzing text and assigning predefined categories based on content. Text classification can also be used for detecting email spam, classifying incoming text according to language, and understanding the important applications of sentiment analysis in commercial fields.

Other examples of NLP tasks include stemming, or reducing words to their stem forms; and lemmatization, or converting words to their base or root forms to identify their meaning. Both stemming and lemmatization are text normalization techniques in NLP to prepare text, words and documents for further processing. Tokenization is another NLP technique, in which a long string of language inputs or words are broken down into smaller component parts so that computers can process and combine the pieces accordingly.

By implementing NLP techniques for success, companies can reap numerous benefits such as streamlining their operations, reducing administrative costs, improving customer service, among others.

Authenticx can aggregate massive volumes of recorded customer conversations by gathering and combining data across silos. This enables companies to collect ongoing, real-time insights to increase revenue and customer retention. Authenticx can also analyze customer data by organizing and structuring data inputs, which can be accessed in a single dashboard and can be customized to reflect business top priorities. Lastly, Authenticx can help enterprises activate their customer interaction data with conversational intelligence tools. Businesses can leverage insights and trends across multiple data sources and provide executives with the right information so they can connect better with their customers.

What is NLP in Machine Learning

Some unfamiliar with all of the nuances of NLP may wonder: what is NLP in machine learning? Machine learning is the study of computer algorithms that can improve automatically through experience and by using data. An NLP machine learning example might be helpful for those who would like to learn more about how exactly NLP, data science and machine learning work altogether.

One of the most important applications of NLP is sentiment analysis, which combines NLP, machine learning and data science to identify and extract relevant information in a particular dataset. Sentiment analysis pertains to the contextual mining of text, which allows businesses to understand the social sentiment pertaining to their brand, products or services.

Another application of NLP is the implementation of chatbots, which are agents equipped with NLP capabilities to decode meaning from inputs. NLP chatbots use feedback to analyze customer queries and provide a more personalized service. Many companies are using chatbots to streamline their workflows and to automate their customer services for a better customer experience. NLP is also being used in speech recognition, which enables machines such as device assistants to identify words or phrases from spoken language and convert them into a readable format. Another use case example of NLP is machine translation, or automatically converting data from one natural language to another.

Authenticx leverages NLP, machine learning and NLP to surface actionable feedback from customer interactions. By combining human and automated analysis of customer data, Authenticx can bring conversational intelligence to organizations. Conversational intelligence extracts meaning from unstructured data to answer customer queries, deliver personalized service and improve customer support.

Natural Language Processing in Artificial Intelligence

What is NLP in AI? Natural language processing in artificial intelligence involves natural language understanding, a branch of AI that uses machine learning to derive meaning from a sentence by analyzing input text. Another subset of NLP is natural language generator, which utilizes AI to generate knowledge from language and create a human language text response based on data input. When it comes to natural language processing in AI, example of use cases include email filtering, or organizing emails based on specialized criteria; smart assistants, which can recognize speech patterns, infer meaning, and provide a relevant response; and predicting sentiment and emotion from text.

There are different types of NLP algorithms to automatically summarize the key points in a given text or document. NLP algorithms can be used for various purposes, including language generation, text summarization and semantic analysis.

Authenticx utilizes AI and NLP to discern insights from customer interactions that can be used to answer questions, provide better service, and enhance customer support. Authenticx can enable companies to understand what is happening during customer conversations, as well as provide context to allow organizations to take action on various issues related to compliance, quality and customer feedback. With Authenticx, businesses can listen to customer voices at scale to better understand their customers and drive meaningful changes in their organizations.

How It Works

Gain a deeper level understanding of contact center conversations with AI solutions.

See a Preview


Pull customer interaction data across vendors, products, and services into a single source of truth.


Collect quantitative and qualitative information to understand patterns and uncover opportunities.


Confidently take action with insights that close the gap between your organization and your customers.

What is NLP Training

What is NLP training, and what does it entail? Generally speaking, an NLP practitioner can be a knowledgeable software engineer who uses tools, techniques, and algorithms to process and understand natural language data.

What does an NLP practitioner do? An NLP practitioner can create NLP algorithms, as well as smooth out and optimize NLP processes and applications. An NLP practitioner can also extract and tailor data to suit business needs.

Authenticx is a software solution created to help humans understand humans. It aims to bring customer voices from various interactions – such as calls, chats and emails – to allow leaders to get in touch with their customers and make better business decisions in order to improve customer experience.

Authenticx utilizes natural language understanding algorithms to surface meaning from context and existing data sources. Authenticx can organize unstructured datasets and identify themes that may be contributing to disruptions in customer journey.

What is NLP Good for

Finally, some may simply wish to know: what is NLP good for? To learn about the benefits and applications of natural language processing in business situations, it may be beneficial to examine various natural language processing examples.

Organizations seeking to understand their customers better can benefit from using Authenticx, which enables businesses to utilize technology to create scalable listening programs using their available data sources.

To get started, companies may need to set specific goals around what they are listening for. Business questions may refer to customer population or a certain business line. Companies may collect samples of customer conversations to determine important criteria such as date range, sample size and variety that would be most meaningful to them.

Aside from the usual customer texts and emails received by contact centers, it may also be helpful to include recorded phone conversations to obtain insights that could be missing in text-based formats. Multiple stakeholders can also take part in the listening exercise.

To aggregate and analyze insights, companies need to look for common themes and trends across customer conversations. Based on these trends, organizations can take actionable insights to provide a better customer experience. The entire process may be repeated to enable businesses to track the progress of their listening programs over time.

With targeted call evaluations and data-backed storytelling, Authenticx can provide organizations valuable context about their customers’ journeys – all within a single platform. Authenticx has evaluated huge volumes of healthcare-focused customer interactions across all aspects of the industry, including life sciences, insurance payers and providers.

Authenticx analyzes customer conversations to surface recurring trends.

Clinical NLP

Natural language processing (NLP) often comes up in conversations about artificial intelligence (AI) technologies, but NLP focuses on allowing systems to ‘understand’, ‘interpret’, and even generate human language. In recent years as AI technologies have exploded in popularity and functional capabilities, NLP has emerged as a vital tool for healthcare professionals and researchers in clinical settings because of NLP’s ability to analyze, extract, and glean insights from unstructured healthcare data – from documents like clinical notes to medical imaging reports, patient-generated healthcare data, and much more. 

Clinical natural language processing refers to the ways in which machine learning technology can help extract meaningful information from various sources of clinical data. While clinical NLP and medical NLP are terms that are oftentimes used interchangeably, they both refer to the same processes. 

Because the overwhelming majority of healthcare data exists in the form of unstructured text, clinical NLP can offer healthcare professionals and medical researchers a variety of benefits. It can be difficult to discern substantive or important information from unstructured datasets, which is where NLP comes in handy. By using advanced techniques, NLP can help parse and extract relevant information to help guide medical professionals in administering the best possible care to patients. 

Some primary applications of clinical NLP include medical coding, clinical decision support, adverse event detection, as well as population health management initiatives. NLP in medical coding refers to the implementation of natural language processing techniques in order to extract procedural and diagnostic information from clinical textual data and convert it into more standardized codes that can help with billing or other administrative purposes

Clinical NLP can also play a critical part in clinical decision support, where the technology can help extract relevant information from clinical notes and other sources of unstructured data in order to provide physicians with accurate, real-time recommendations and even alerts related to patient care. When healthcare providers have a greater arsenal of tools that are their disposal, patient outcomes may improve, and the overall quality of care that patients have access to will likely increase. 

Additionally, clinical NLP applications can also support population health management applications. By analyzing gross volumes of clinical text data, NLP algorithms can identify certain trends or patterns in patient populations, which can then be used to help develop more effective interventions and improve overall population health, which substantially benefits everyone – not just doctors and their patients. 

Natural Language Processing

We covered some of the essentials related to natural language processing in the first section, but what is NLP exactly? The basic principles of NLP – natural language processing – involve analyzing human language from numerous perspectives. NLP algorithms are capable of gleaning information about syntax, semantics, and pragmatics, and they break down the language into smaller pieces like words, phrases, and sentences, in order to apply statistical and machine learning techniques to understand the meaning and context of the language

Such a robust piece of technology can be applied to the context of clinical data in a handful of ways. For instance, NLP can be leveraged to extract important information from electronic health records – information like patient demographics, previous diagnoses, and effective treatments. This information can then be used to support healthcare professionals in clinical decision-making, and ultimately improve the likelihood of positive patient outcomes. 

NLP projects with source code are also widely available, making it easier now than it ever has been before for healthcare organizations and medical researchers to apply NLP techniques to clinical datasets. These projects often include pre-trained models and libraries that can be customized or personalized for specific use cases like clinical text classification or clinical coding. 

Natural language processing for healthcare can also be utilized to help analyze unstructured data, identify patients that would be good candidates for medical trials, and help monitor adverse events in real-time, alerting healthcare professionals instantaneously when a patient experiences a serious or life-threatening event. 

Medical NLP Dataset

A medical NLP dataset can be a critical resource for training and evaluating machine learning models within the healthcare industry. These datasets typically consist of a large volume of medical text, including clinical notes, radiology reports, pathology reports, and discharge summaries, among many others. And because the process is largely automated, AI technologies are able to process much more information than human data entry specialists could, given the same amount of time. 

Sources of data for medical NLP datasets may vary somewhat, but they usually come from research studies, clinical trials, and electronic health records. Such datasets are frequently collected from multiple healthcare institutions, which may yield a variety of different textual formats, including free-text, semi-structured, and structured data. 

In order to create high-quality medical NLP datasets, data annotation, and accurate labeling are absolutely crucial. This process can involve tagging documents and text with relevant labels, such as named entities, medical concepts, and relationships between multiple concepts. While this process can be tedious, time-consuming, and requires domain expertise, the combined efforts generally yield more accurate and reliable datasets. 

In recent years, open-source medical NLP libraries have grown in popularity because of their ability to provide researchers and other healthcare professionals with access to pre-trained models and tools to help create and annotate a medical NLP library. The Clinical NLP Workshop also provides a global platform for researchers to share medical NLP datasets and evaluate machine learning models on a standardized dataset. 

Medical NLP datasets play a significant role in modern healthcare applications by enhancing the development and evaluation of machine-learning models. The data pre-processing techniques, the sources of data, and the process of data annotation and/or data labeling are essential components in creating high-quality NLP datasets. And resources like the open-source medical NLP libraries and the Clinical NLP Workshop provide researchers and healthcare experts with invaluable resources to create and evaluate medical NLP models. 

How It Works

Gain a deeper level understanding of contact center conversations with AI solutions.

See a Preview


Pull customer interaction data across vendors, products, and services into a single source of truth.


Collect quantitative and qualitative information to understand patterns and uncover opportunities.


Confidently take action with insights that close the gap between your organization and your customers.

Medical NLP Models

Medical NLP models are machine learning algorithms that are capable of leveraging natural language processing technology in order to support healthcare providers by streamlining workflows, improving patient care, and helping medical professionals make better-informed decisions. And better-informed decisions often lead to better health outcomes.

One of the main benefits of medical NLP models is their ability to automate and streamline tasks that were traditionally performed manually. Since NLP models can be effectively leveraged to extract significant information (patient diagnoses, symptoms, treatments, etc.) from medical notes, healthcare providers can populate EHRs with this information and reduce the amount of time, effort, and resources needed to make valuable information more accessible to physicians trying to provide the best quality care. 

An additional benefit of medical NLP models is their capabilities in regard to improving patient care. By analyzing scores of clinical text data, NLP models can rapidly identify patterns or trends that human reviewers sometimes miss. In some cases, NLP models can identify patients who are at an elevated risk of developing a particular disease or cancer, allowing the patient’s healthcare providers to take preventative measures and ultimately improve patient outcomes. 

NLP for EHR is yet another example of an important application of medical NLP models. EHRs are usually populated by unstructured data, which can make it difficult for healthcare professionals to extract the relevant information they need. By relying on NLP models to extract and analyze data from EHRs, healthcare providers can glean significant insights into patient health, disease progression, as well as treatment outcomes. NLP medical notes can also be analyzed by machine learning algorithms to extract information that can further support patient care processes. 

Benefits of Natural Language Processing in Healthcare

One of the most significant benefits of natural language processing in healthcare is the potential to enhance clinical decision-making by extracting important information from large amounts of unstructured data. Additionally, NLP technologies can help to improve patient outcomes by providing personalized care based on patient-specific data. 

NLP in healthcare research papers has also been an extremely valuable tool for healthcare providers and medical researchers. By automating the analysis aspect of clinical notes and other troves of unstructured data, researchers can rapidly conduct large-scale studies like never before. Additionally, NLP can help identify patients for clinical trials, which can save healthcare organizations both time and valuable resources. 

Healthcare providers must also take into account the ethical considerations regarding NLP technology when seeking to implement natural language processing in healthcare applications. For example, patient privacy must remain protected and confidential, and when healthcare providers utilize NLP technologies, patients must receive adequate notice. Furthermore, healthcare providers must ensure that algorithms used in NLP solutions are as fair and unbiased as possible. 

Natural language processing in medicine is also useful in the integration of electronic health records (EHRs). NLP can be leveraged to automatically extract relevant information from EHRs like medical histories, patient demographics, previous diagnoses, and more, which can then be used to help healthcare providers make informed clinical decisions. 

Ultimately, natural language processing is a powerful tool that’s already begun to revolutionize the healthcare industry. By enabling the analysis of unstructured health data and facilitating better clinical decision-making, NLP can improve patient outcomes and contribute greatly to medical research initiatives.

Copy link
Powered by Social Snap