Natural language processing, or NLP for short, is most effective described as “AI for speech and text.” The magic powering voice commands, speech and text translation, sentiment analysis, text summarization, and a lot of other linguistic apps and analyses, natural language processing has been enhanced radically through deep understanding.
The Python language gives a practical front-end to all types of device understanding like NLP. In truth, there is an embarrassment of NLP riches to opt for from in the Python ecosystem. In this write-up we’ll explore every single of the NLP libraries readily available for Python—their use scenarios, their strengths, their weaknesses, and their normal degree of level of popularity.
Be aware that some of these libraries offer bigger-degree versions of the very same functionality exposed by many others, creating that functionality less complicated to use at the cost of some precision or general performance. You are going to want to opt for a library effectively-suited equally to your degree of skills and to the mother nature of the challenge.
The CoreNLP library — a solution of Stanford University — was designed to be a output-ready natural language processing alternative, able of providing NLP predictions and analyses at scale. CoreNLP is written in Java, but many Python deals and APIs are readily available for it, like a indigenous Python NLP library known as StanfordNLP.