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Human speech is not at all precise and is often ambiguous and can also vary in meaning depending on the context, tone of the speaker, slang, regional dialects etc. However, machines can be trained to learn and understand natural languages
Applications of natural language processing
Natural language processing has a multitude of applications around technologies where human-machine interaction happens. Some of the predominant examples are search, voice recognition, translation and voice assistants.
Enterprise search: Most of the recent developments around NLP is focused at providing a better search experience, especially to the enterprise applications. This essentially involves enabling users to enter in a query in a natural language and get the proper response from the machine as if it were a human being. For this to work, the machine should be able to interpret the natural language and this is exactly where NLP does its magic.
Interpreting free text: NLP can be used to make free text analyzable for machines. Since there is a host of information in text files, such as the medical records of patients, if a machine is capable of analyzing it, the workload for physicians can be considerably reduced and can even open doors to interesting developments in the field of medicine. With deep learning-based NLP models, the analysis of free text is a systemic way is now possible to a certain extent.
Building chatbots: Chatbots have been making it easier for businesses to handle the sales flow via automation and even cut down the need for human customer service agents. In order to create intelligent and more interactive chatbots, the training aspect has to be significantly improved. However, this will require huge amounts of training data. NLP can enhance the chatbot experience for the users and well as businesses.
Where to find datasets for NLP?
While natural language processing is still its nascent stages, the research and development in this field is booming. The biggest factor involved in the development of chatbots is the availability of large data sets from relevant sources. If the NLP system you are developing is aimed at the travel industry, you would want to feed it with data from the travel industry.
Web is the biggest repository of data and most NLP use cases can be covered using web data because of this. However, extracting this data from the web using an in-house crawler might prove to be a very demanding activity which can drain your time and focus on the core activity which is the development of NLP system.
DataStock can be used to build NLP systems as it provides pre-crawled, clean and ready-to-use datasets from various industry verticals at a nominal price. All the data sets were extracted for our clients for their unique requirements through our custom web scraping solution. These are ideal for NLP use cases as they cover millions of records with data fields for large textual data.
Below are the various datasets on DataStock that can be used for NLP applications: