Using computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.
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- Your host as usual is Tobias Macey and today I’m interviewing Rami Al-Rfou about Polyglot, a natural language pipeline with support for an impressive amount of languages
- How did you get introduced to Python?
- Can you start by describing what Polyglot is and your reasons for starting the project?
- What are the types of use cases that Polyglot enables which would be impractical with something such as NLTK or SpaCy?
- A majority of NLP libraries have a limited set of languages that they support. What is involved in adding support for a given language to a natural language tool?
- What is involved in adding a new language to Polyglot?
- Which families of languages are the most challenging to support?
- What types of operations are supported and how consistently are they supported across languages?
- How is Polyglot implemented?
- Is there any capacity for integrating Polyglot with other tools such as SpaCy or Gensim?
- How much domain knowledge is required to be able to effectively use Polyglot within an application?
- What are some of the most interesting or unique uses of Polyglot that you have seen?
- What have been some of the most complex or challenging aspects of building Polyglot?
- What do you have planned for the future of Polyglot?
- What are some areas of NLP research that you are excited for?
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