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Tools Using In Natural Language Generation
There is no end to end Natural Language Generation library tools or module. You can use those libraries that help you write your content selection module or your surface realization module. There is no module for the discourse planning/document planning or microplanning stage.
If you are using Java you can use simple Natural Language Generation tools or openCCG for surface realization. For the planning stage, you have to write your own module. For lexicalization, you can use those NLP functions. In Python, there is a library called Pattern that has a lot of lexical transformation functions. A pattern is also a good tool for your other NLP needs.
Natural language processing (NLP), the technology that powers all the chatbots, voice assistants, predictive text, and other speech/text applications that permeate our lives, has evolved significantly in the last few years. There are a wide variety of open source NLP tools out there, so I decided to survey the landscape to help you plan your next voice- or text-based application.
It would be easy to argue that Natural Language Tools (NLTK) is the most full-featured tool of the ones I surveyed. It implements pretty much any component of NLP you would need, like classification, tokenization, stemming, tagging, parsing, and semantic reasoning. It also supports many languages. However, it represents all data in the form of strings, which is fine for simple constructs but makes it hard to use some advanced functionality.
The e-commerce stores. Here is the link to the solution ( ). Pricing starts at $40 for 100 descriptions. For my previous project for an apparel store, I got 2000 descriptions written for just $550. solution costs around 10-20 cents per description for a lot size of 1000+. It will be in the range of 5–7 cents when your lot size is above 25,000. The content is decent for