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  • What is the best stemming method in Python? - Stack Overflow
    7 Stemmers vary in their aggressiveness Porter is one of the monst aggressive stemmer for English I find it usually hurts more than it helps On the lighter side you can either use a lemmatizer instead as already suggested, or a lighter algorithmic stemmer The limitation of lemmatizers is that they cannot handle unknown words
  • nlp - Stemmers vs Lemmatizers - Stack Overflow
    Stemmers are much simpler, smaller, and usually faster than lemmatizers, and for many applications, their results are good enough Using a lemmatizer for that is a waste of resources
  • What is the difference between lemmatization vs stemming?
    However, stemmers are typically easier to implement and run faster, and the reduced accuracy may not matter for some applications For instance: The word "better" has "good" as its lemma This link is missed by stemming, as it requires a dictionary look-up
  • java - What are the major differences and benefits of Porter and . . .
    At the very basics of it, the major difference between the porter and lancaster stemming algorithms is that the lancaster stemmer is significantly more aggressive than the porter stemmer The three major stemming algorithms in use today are Porter, Snowball (Porter2), and Lancaster (Paice-Husk), with the aggressiveness continuum basically following along those same lines Porter is the least
  • Which word stemmer should I use in nltk? - Stack Overflow
    And so I've been exploring nltk stem only to realize that there are 4 different stemmers I'd like to ask the stackoverflow linguists whether LancasterStemmer, PorterStemmer, RegexpStemmer, RSLPStemmer, or WordNetStemmer is best preferably with some justification
  • Python nltk stemmers never remove prefixes - Stack Overflow
    I'm trying to preprocess words to remove common prefixes like "un" and "re", however all of nltk's common stemmers seem to completely ignore prefixes: from nltk stem import PorterStemmer,
  • How do I do word Stemming or Lemmatization? - Stack Overflow
    The Porter stemmer is appropriate to IR research work involving stemming where the experiments need to be exactly repeatable Dr Porter suggests to use the English or Porter2 stemmers instead of the Porter stemmer The English stemmer is what's actually used in the demo site as @StompChicken has answered earlier
  • postgresql - Does Rusts tool Tantivy support Snowball stemmers like in . . .
    What stemmers does it use under the hood? Lift the hood to find that Tantivy currently depends on the stemmers in rust-stemmers [dependencies] # rust-stemmers = "1 2 0" In turn, rust-stemmers is well documented as providing Snowball stemmers for multiple languages: This crate implements some stemmer algorithms found in the snowball project which are compiled to rust using the rust-backend
  • Comparison of Lucene Analyzers - Stack Overflow
    In general, any analyzer in Lucene is tokenizer + stemmer + stop-words filter Tokenizer splits your text into chunks, and since different analyzers may use different tokenizers, you can get different output token streams, i e sequences of chunks of text For example, KeywordAnalyzer you mentioned doesn't split the text at all and takes all the field as a single token At the same time
  • How to configure stemming in Solr? - Stack Overflow
    Why would you have two stemmers? Try removing EnglishPorterFilterFactory (deprecated) from both of your analyzer types, rebuild the index and then try whether search for American will yield America If that wont work, the other thing you can try is to remove both of your stemmer filters and add SnowballPorterFilterFactory with language="English" instead





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