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Natural Language
Processing Solutions
We help you leverage the power of natural language processing (NLP) technologies to build smart solutions that can transform your business.
Common NLP Applications
Filters
Translation
Analysis
Assistants
Checks
Text
Detection
Results
Detection
NLP Solutions for Industries
Healthcare
- Medical prescription analyzer
- Smart assistants (medical transcribers / ordering assistants / billing)
- Root cause analysis
Manufacturing
- Inventory management
- Process automation
- Operation optimization
- Compliance tracking
Banking
- Loan application fraud detection
- Risk assessment
- Financial sentiment analysis
- Virtual assistant
Construction
- Structural risk analysis
- Information retrieval
- Compliance checking
Retail
- Invoice processing
- Cognitive searches
- Targeted advertising
- Customer review analysis
Healthcare
Utilizing NLP, healthcare processes can be digitalized, examples of which include clinical trial matching and clinical documentation. A virtual scribe lessens the burden of documentation on doctors, allowing them.. to give their patients their undivided attention.Scribes can be on-site or off-site and contribute through phone or video conference. They help automate most of the recording for EHR. Virtual scribes also assist in recording laboratory results, radiology results, and clinical charting.
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Retail
Intelligent chatbots utilize text recognition and semantics to decipher user intent and provide precise answers to customer queries. Customer reviews and feedback can be channeled into focused marketing initiatives,.. thereby improving customer satisfaction and ROI. Customers can use in-store bots for self-checkouts and narrow their search using guided product information, both of which take the in-store shopping experience to new levels. Marketers can upsell products via chat based on previous customer purchases.
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Finance
NLP models can be used to avert stock market manipulation and bank loan fraud. Through pattern recognition, money laundering and other fraudulent activities can be identified and stopped in their tracks... With the help of Intelligent Data Processing, banks can speed up the verification of KYC documents, mortgage applications, etc. to provide a more satisfying customer experience.Named entity recognition can be used to extract pertinent information from loan agreements, which can be utilized to estimate risk before sanctioning loans. Using NLP models like FinBERT, finance sentiment analysis can be done to make predictions about stock market performance.
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Healthcare
Utilizing NLP, healthcare processes can be digitalized, examples of which include clinical trial matching and clinical documentation. A virtual scribe lessens the burden of documentation on doctors, allowing them to give their patients their undivided attention.
Scribes can be on-site or off-site and contribute through phone or video conference.
They help automate most of the recording for EHR. Virtual scribes also assist in recording laboratory results, radiology results, and clinical charting.
Retail
Intelligent chatbots utilize text recognition and semantics to decipher user intent and provide precise answers to customer queries. Customer reviews and feedback can be channeled into focused marketing initiatives, thereby improving customer satisfaction and ROI.
Customers can use in-store bots for self-checkouts and narrow their search using guided product information, both of which take the in-store shopping experience to new levels. Marketers can upsell products via chat based on previous customer purchases.
Finance
NLP models can be used to avert stock market manipulation and bank loan fraud. Through pattern recognition, money laundering and other fraudulent activities can be identified and stopped in their tracks. With the help of Intelligent Data Processing, banks can speed up the verification of KYC documents, mortgage applications, etc. to provide a more satisfying customer experience.
Named entity recognition can be used to extract pertinent information from loan agreements, which can be utilized to estimate risk before sanctioning loans. Using NLP models like FinBERT, finance sentiment analysis can be done to make predictions about stock market performance.
NLP Applications in QBurst
NLP Applications in QBurst
The use of NLP for internal processes in addition to client projects gives us an advantage. We have utilized NLP at QBurst to automate routine tasks and increase team efficiency. In HR department, the CV parsing service extracts personal information, career history, educational qualification, and skills from candidate CVs to speed up the candidate screening process.
We have provided NLP-based solutions for different sectors, ranging from the construction industry to disaster management. Along with client projects, our engineers also keep experimenting and continually contribute their findings to open sources such as GitHub.
How Does NLP Work?
NLP is a branch of Artificial Intelligence concerned with teaching machines to understand and interpret human language in its natural form. Different methods are utilized to train the models in order to do this. The code can be adjusted to meet our demands using specific tools and algorithms.
To translate the input language into a machine-readable format, it must go through several pre-processing steps, including lemmatization, stemming, and tokenization. Additionally, with learning models like BERT, the computer is educated to comprehend the language and offer insightful commentary from the input.
NLP Approaches
1. Rule-based
The rule-based approach emphasizes parsing. It resembles a "fill in the blanks" strategy. This method can be utilized for repetitive activities like spell checks and grammar correction.
2. Traditional machine learning method
Repositories of data for training NLP models are available today. Making use of this corpus, NLP models are trained using statistical methods such as SVM.
3. Neural networks
Neural networks are replacing more traditional techniques as we have more data and processing capability. This approach differs from conventional machine learning in the methods used to train the model. A traditional model makes decisions based on what it has learned from the data but a neural network arranges algorithms in such a way that it can make decisions on its own.
1. Rule-based
The rule-based approach emphasizes parsing. It resembles a "fill in the blanks" strategy. This method can be utilized for repetitive activities like spell checks and grammar correction.
2. Traditional machine learning method
Repositories of data for training NLP models are available today. Making use of this corpus, NLP models are trained using statistical methods such as SVM.
3. Neural networks
Neural networks are replacing more traditional techniques as we have more data and processing capability. This approach differs from conventional machine learning in the methods used to train the model. A traditional model makes decisions based on what it has learned from the data but a neural network arranges algorithms in such a way that it can make decisions on its own.
