Question 1

How to train a custom POS tagger with Postagga for a new language?

Accepted Answer

You need an annotated text corpus in the required format (a vector of tagged sentences), then use the postagga.trainer/train function. However, creating such corpora is manual and time-consuming, and you must ensure compliance with licensing for derived works.

Question 2

Postagga vs Stanford CoreNLP for Clojure projects

Accepted Answer

Postagga is better for lightweight, embeddable parsers with no dependencies, ideal for ClojureScript browsers. Stanford CoreNLP offers more advanced features and broader language support but requires Java integration and is heavier. Choose Postagga for simplicity and portability in Clojure ecosystems.

Question 3

Can Postagga handle real-time chat messages efficiently?

Accepted Answer

It can process small to medium volumes with its rule-based parsers, but performance depends on model size and rule complexity. For high-throughput chat systems, you might need to optimize tokenizers and cache models to avoid latency from large variable realizations.

Question 4

What languages does Postagga support beyond English and French?

Accepted Answer

Postagga itself is language-agnostic and can support any language if you provide a trained model. However, the pre-trained models are only for English and French, so adding new languages requires sourcing or creating annotated corpora and handling tokenization specifics.

Question 5

Is Postagga suitable for sentiment analysis tasks?

Accepted Answer

Not directly, as it focuses on part-of-speech tagging and rule-based parsing for structured data extraction. For sentiment analysis, you'd need to build custom rules mapping phrases to sentiment scores, which might be less accurate than machine learning-based approaches.

Question 6

How do I debug parsing errors in Postagga rules?

Accepted Answer

Errors are reported in the parse result's :errors field, which maps rules to failed steps and states. The README notes this can be large, so inspect carefully. Testing with small sentences and incremental rule development is recommended due to the complex state-machine logic.

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