NLP

Gain better insights from your unstructured data



Retail

Create customer-friendly stores with personalized bots. Build customer interfaces to up-sell and answer product questions. Analyze customer reviews.

FinTech

Uncover meaningful insights from under-used content. Recognize speech and parse intent using voice assistants. Support compliance processes.

Healthcare

Search, analyze and interpret patient records. Glean insights from unstructured Clinical Trials Data. Solve population health using root cause analysis.

Smart Cities

Reduce support cists by helping citizens find and fill the correct forms. Make every citizen’s voice heard with contextual understanding.

What is NLP?

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is an emerging technology that drives many forms of AI. NLP strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do.


The end goal of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. It’s at the core of tools we use on a daily basis – from translation software, chatbots, e-mails filters, and search results, to predictive text, smart assistants, and social media monitoring tools. Companies can outshine competition with NLP in areas such as:

  • Sentiment Analysis
  • Text Classification
  • Market Intelligence
  • Intent Classification
  • Virtual Assistance
Life Sciences

Optimize Process From Molecule to Market

Life science firms face massive challenges understanding massive amounts of unstructured data. From drug discovery through development, and into delivery, insight is needed at every stage, to answer questions, get through gates, and achieve milestones.



Understanding gene-disease associations, pathways, and systems, is critical for drug discovery and basic research. Much of the data to support these decisions is buried in unstructured text, both in public databases, like PubMed and internal sources. 


To start basic research, R&D scientists have to identify the biological origin of a disease, and potential targets for intervention. This requires a comprehensive understanding of the genes involved in the disease pathway, so a systematic review of the public domain literature is important. An environment in which “first to file” has now largely replaced “first to invent” demands a more sophisticated and effective patent mining technology. NLP technologies can automate and accelerate science to a great degree. 

Access to Knowledge Embedded in Text

Semantic analysis of Research and Clinical data to to speed time to market.

Track Market Evolution

Improve retargeting efforts by analyzing large cohorts of DNA sequence data along with other biomedical and imaging datasets.

Commercial

Optimize interactions with Clinicians. Incorporate Patient Voice in Product Iterations.
Provider

Tap into Your Wealth of Data For Better Patient Outcomes

Clinicians report that 50% of the patient time is spent on EHR. The pain is endless. Gathering insight from clinical notes remains one of the areas of untapped healthcare intelligence with tremendous potential – both for the patient and the provider’s bottom line. 


NLP has the potential to harness relevant insights and concepts from data that was previously considered buried in text form. Physicians spend a lot of time inputting the how and the why of what’s happening to their patients into chart notes. These notes aren’t easily extractable in ways the data can be analyzed by a computer. With NLP, physicians can find information in unstructured medical literature to support care decisions and can even uncover diseases that may not have been previously coded.

Treatment Insights

Reduce subjectivity in decision-making & deliver better, more efficient care to patients.

Sentiment Analysis

Turn qualitative data into quantitative business intelligence about patient experience..

Reduce Time Spent on EMR / EHR

Auto transcribe Physician's conversation with patients and improve quality of care.
Payer

Power to Predict Outcomes More Accurately

Health plans and payers rely on medical record review for multiple different business-critical processes. vital member insights that can be gained from medical records and other unstructured healthcare data sources are too important to be ignored. 


NLP can improve efficiency in areas such as Medicare risk adjustment, clinical review/medical necessity, risk stratification, and HEDIS medical record review for hybrid measures. For example, by using sentiment analysis, Health Plans can conduct Patient Surveillance to identify high-risk population members & improve outcomes.

Prior Authorization

Determine Prior Authorizations quickly and reduce overhead and delay in care delivery.

Medicare Advantage Risk Adjustment

Identify specific disease comorbidities to increase revenue potential.

HEDIS™ Quality Measures

Increase numerator value by identifying and closing gaps by continuous year-round assessment.
Financial Services

Helping Traders get an Edge.

Traders and investment managers have numerous sources to sift through, such as research reports, company filings, and transcripts of quarterly earnings calls. NLP models can be trained to review this unstructured content and spot issues or trends that might impact financial markets. 


A company’s quarterly earnings call includes information about prior quarter, outlook for the future, M&A announcements, and a Q&A session. The tone of voice, how the questions were answered can reflect on the firm’s stock price. Speech recognition is a key piece of the analysis that can be automated with NLP.


Sentiment analysis, another facer of NLP,  can help extract the subjective meaning from text sufficiently well to be able to determine its attitude or sentiment. It is an ideal tool for reviewing unstructured content about a particular company to look for inconsistencies and anomalies.

Enhance Alpha Generation

Automate routine analysis of earnings reports and news,

Search & Discovery

Finding relevant information in large volumes of documents.

Customer Care

Chatbots to answer customer queries and understand their needs.
Retail / CPG

The Future of Shopping

It is a challenge to help in-store shoppers with their purchases, especially during peak times. NLP solutions s can ensure that customers have a hassle-free, time-efficient, and unique shopping experience at the store.


NLP technology can be deployed in various ways. As Touch Screens thought the store, acting as virtual assistants, as roaming physical humanoid robots. For online e-commerce stores, NLP can enhance the shopping experience. NLP solutions can analyze a customer’s search history, recommend products, answer product specific questions, or help them find a product at the nearest brick & mortar store. 


By reducing the dependence on human resources, NLP solutions can substantially reduce costs, while working for you 24x7. NLP adds a human touch as the experience is akin to talking to a human customer service rep, resulting in improved user satisfaction and high customer retention. These solutions can also save time for customers as well as businesses.

Smarter Customer Experience

Anticipate customer needs, respond in real-time, and create personalized shopping itineraries.

Forecast Trends

Extract information from Social Media on popular products and fashion trends.

Generate Product Descriptions

Use brand name, price, specs, to create product description content for catalogs.
Smart Cities

Powering Smart Cities with Intelligent Data

We Smart City is no longer an abstract concept. The COVID-19 pandemic has disrupted the lives of nearly every human being across the globe. Many local communities were ill-prepared, under-resourced, or distressed even prior to the pandemic. Using unstructured data analysis, some smart cities identified Covid-19 hotspots within their jurisdiction using publicly-available health and socioeconomic data, and put plans in place to mitigate it, saving lives and resources.


Smart cities run on data and they can use chatbots to improve delivery of services for the citizens they serve. Most citizens are already familiar with conversational platforms like, Google Assistant, Alexa, and Siri. Cities can use similar conversational agents to drive more citizen-centric services.


For example, they can provide answers to an array of user questions and continuously learn from the interactions — even pre-empting questions as NLP captures the most frequently used terms; shifting the burden of dealing with complexity from the users to the technology.

Public Opinion Mining

Sentiment Analysis to alert various departments to take action.

Immersive City Guide

Powerful and immersive resource for exploring and navigating around the city for residents and visitors alike.

Q&A

Engaging & Easy to use city-wide Q&A platform

From unstructured Data to Actionable Insights with NLP

Transform your business with natural language processing. NLP helps your firm process, understand, and generate text insights. Use NLP sentiment analysis to discover your customers’ perception of your brand. NLP enables more natural conversations, more efficient operations, reduced costs, higher customer satisfaction, and improved analysis. Convert your unstructured data into actionable insights with NLP techniques.



Xenolytix provides Natural Language Processing consulting and implementation services to help enterprises gain insights from unstructured data. We bring a diverse set of research and development expertise, scientific rigor and, a deep knowledge of state-of-the-art techniques to design, build, and 

LET'S DISCUSS

NLP FAQ

Got a question? We’re here to help.

  • What is Natural Language Processing (NLP)?

    Put simply, NLP is a computer’s ability to read/listen and understand.  It is broadly defined as the automatic manipulation of natural language, like speech and text, by software. 

    NLP is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.


  • Why is NLP important?

    Natural Language Processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. 


    NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

  • How does NLP work?

    Whether the language is spoken or written, natural language processing uses artificial intelligence to take real-world input, process it, and make sense of it in a way a computer can understand. There are two main phases to natural language processing: data preprocessing and algorithm development.


    Data preprocessing involves preparing and "cleaning" text data for machines to be able to analyze it. There are several ways this can be done, including:

    • Tokenization - Text is broken down into smaller units to work with.
    • Stop Word Removal - Common words are removed from text so unique words that offer the most information about the text remain.
    • Lemmatization & Stemming - Words are reduced to their root forms to process.
    • Part-of-Speech Tagging - Words are marked based on the part-of speech (nouns, verbs, adjectives).

    Once the data has been preprocessed, an algorithm is developed to process it. Machine Learning algorithms learn to perform tasks based on training data they are fed, and adjust their methods as more data is processed. Using a combination of machine learning, deep learning, and neural networks, NLP algorithms hone their own rules through repeated processing and learning.

  • What is Semantic Analytics?

    Semantic Analytics is the process of drawing meaning from text. It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context.


    From a data processing point of view, semantics are “tokens” that provide context to language—clues to the meaning of words and those words’ relationships with other words. From these “tokens” the expectation is for the machine to look beyond the individual words used to identify the true meaning of what’s being said as a whole.

  • What is Sentiment Analysis?

    Sentiment Analysis is a Natural Language Processing technique used to determine whether data is positive, negative, or neutral. 

    Sentiment analysis is often performed on textual and voice data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.