wordnet

This question already has an answer here: How to get synonyms from nltk WordNet Python 5 answers The code to get the synonyms of a word in python is say: from nltk.corpus import wordnet dog = wordnet.synset('dog.n.01') print dog.lemma_names >>['dog', 'domestic_dog', 'Canis_familiaris'] However dog.n.02 gives different words. For any words i can't know how many words there may be. How can i return all of the synonyms for a word? Using wn.synset('dog.n.1').lemma_names is the correct way to access the synonyms of a sense . It's because a word has many senses and it's more appropriate to list

i would like a python library function that translates/converts across different parts of speech. sometimes it should output multiple words (e.g. "coder" and "code" are both nouns from the verb "to code", one's the subject the other's the object) # :: String => List of String print verbify('writer') # => ['write'] print nounize('written') # => ['writer'] print adjectivate('write') # => ['written'] i mostly care about verbs <=> nouns, for a note taking program i want to write. i.e. i can write "caffeine antagonizes A1" or "caffeine is an A1 antagonist" and with some NLP it can figure out they

I want to check in a Python program if a word is in the English dictionary. I believe nltk wordnet interface might be the way to go but I have no clue how to use it for such a simple task. def is_english_word(word): pass # how to I implement is_english_word? is_english_word(token.lower()) In the future, I might want to check if the singular form of a word is in the dictionary (e.g., properties -> property -> english word). How would I achieve that? Katriel For (much) more power and flexibility, use a dedicated spellchecking library like PyEnchant . There's a tutorial , or you could just dive

Natural Language Processing (NLP), especially for English, has evolved into the stage where stemming would become an archaic technology if "perfect" lemmatizers exist. It's because stemmers change the surface form of a word/token into some meaningless stems. Then again the definition of the "perfect" lemmatizer is questionable because different NLP task would have required different level of lemmatization. E.g. Convert words between verb/noun/adjective forms . Stemmers [in]: having [out]: hav Lemmatizers [in]: having [out]: have So the question is, are English stemmers any useful at all today?