Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Utilize natural language processing to derive insights from unstructured text without needing machine learning expertise, leveraging a suite of features from Cognitive Service for Language. Enhance your comprehension of customer sentiments through sentiment analysis and pinpoint significant phrases and entities, including individuals, locations, and organizations, to identify prevalent themes and trends. Categorize medical terminology with specialized, pretrained models tailored for specific domains. Assess text in numerous languages and uncover vital concepts within the content, such as key phrases and named entities encompassing people, events, and organizations. Investigate customer feedback regarding your brand while analyzing sentiments related to particular subjects through opinion mining. Moreover, extract valuable insights from unstructured clinical documents like doctors' notes, electronic health records, and patient intake forms by employing text analytics designed for healthcare applications, ultimately improving patient care and decision-making processes.
Description
The insurance sector focuses on achieving optimal rates and effectively managing risk. In such a competitive landscape, reducing manual processes is essential to distinguish ourselves from other industry players. A significant workforce is often necessary to sift through, interpret, categorize, analyze, and disseminate information for underwriting and support activities. Much of this information is unstructured and text-based, requiring manual examination. Scaling operations typically involves hiring additional personnel or resorting to outsourcing solutions. It is vital to filter and classify complaints based on their subject matter and severity level. Automotive businesses collect these complaints through various channels, including emails, feedback forms, and comments. Lymba’s Underwriting and Support NLP solution addresses the text-heavy challenges by converting data into actionable insights; this efficiency not only saves time and resources but also facilitates the initial review process, ultimately enhancing overall productivity and decision-making. By leveraging such technology, companies can focus more on strategic initiatives rather than getting bogged down by manual data handling.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Azure Marketplace
No
TAS Insight Engine
No
Unremot
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
Vendor Details
Company Name
Lymba
Founded
2005
Country
United States
Website
www.lymba.com
Product Features
Natural Language Processing
Co-Reference Resolution
Yes
In-Database Text Analytics
Yes
Named Entity Recognition
Yes
Natural Language Generation (NLG)
Yes
Open Source Integrations
Yes
Parsing
No
Part-of-Speech Tagging
Yes
Sentence Segmentation
Yes
Stemming/Lemmatization
Yes
Tokenization
No
Product Features
Data Extraction
Disparate Data Collection
No
Document Extraction
No
Email Address Extraction
No
IP Address Extraction
No
Image Extraction
No
Phone Number Extraction
No
Pricing Extraction
No
Web Data Extraction
No
Natural Language Processing
Co-Reference Resolution
No
In-Database Text Analytics
No
Named Entity Recognition
No
Natural Language Generation (NLG)
No
Open Source Integrations
No
Parsing
No
Part-of-Speech Tagging
No
Sentence Segmentation
No
Stemming/Lemmatization
No
Tokenization
No