- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
Natural Language Processing
How Is External Data Used in Natural Language Processing?
Natural language processing (NLP) fuses mathematics and computer science with artificial intelligence. It takes in spoken, written, and image-based language and applies statistical rules to process the language and turn it into commands or to enable computers to communicate with people.
Unfortunately, these machine learning models face difficulties in this use case theme: mainly, they cannot read tone. However, they use contextual clues in the surrounding text to compensate.
How are Machine Learning Models Used in Natural Language Processing?
In the earliest days of natural language processing, developers applied manually-generated rules-based algorithms to language. However, the field advanced to the use of statistical models—then to neural models. In contrast with older rules-based models, these programs can take in huge amounts of data and generate outputs (conversations, translations) based on the most likely results without having to tag individual parts of speech.
Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?
Master of Code
Founded in 2005, Master of Code develops NLP products, especially chatbots, compatible with Facebook, WhatsApp, Google, and more. They also count Amazon, Azure, and IBM among their partners.
SoundHound
A leader in conversational AI applications in the IoT field, SoundHound enables people to discover and identify songs from snippets of music. They also enable people to give orders to their household appliances.
Grammarly
Grammarly offers free text-based NLP programs that help people improve their spelling and grammar. It even offers analysis and suggestions for improving perceived tone in written speech.
chatbot datasets
Connected Datasets
PipeCandy eCommerce Leads & Insights for Fulfillment
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar Predictive Credit Technology
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.