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)
I am training the NaiveBayesClassifier in Python using sentences, and it gives me the error below. I do not understand what the error might be, and any help would be good.
I used NLTK’s ne_chunk to extract named entities from a text:
I needed to compute the Unigrams, BiGrams and Trigrams for a text file containing text like:
Hello world.Hi I am here today.
Using NLTK and WordNet, how do I convert simple tense verb into its present, past or past participle form?
I am using nltk, so I want to create my own custom texts just like the default ones on nltk.books. However, I’ve just got up to the method like
I want to calculate the term-frequencies of words in a text corpus. I’ve been using NLTK’s word_tokenize followed by probability.FreqDist for some time to get this done. The word_tokenize returns a list, which is converted to a frequency distribution by FreqDist. However, I recently came across the Counter function in collections (collections.Counter), which seems to be doing the exact same thing. Both FreqDist and Counter have a most_common(n) function which return the n most common words. Does anyone know if there’s a difference between these two? Is one faster than the other? Are there cases where one would work and the other wouldn’t?
How to check whether a sentence is valid in Python?
How do I detect what language a text is written in using NLTK?