lemmatization

I'm using LemmaGen ( http://lemmatise.ijs.si ) for text lemmatisation. I've successfully used it by running the following statement in the command line. $lemmatize -l $./data/lemmatizer/lem-m-en.bin input.txt output.txt However, I actually want to use it as a library in my C++ project programatically. Any one knows how to use LemmaGen C++ API? Thanks! Or anyone can suggest other C++ lemmatisation library that can be used in C++ programmatically? Please correct me if I'm asking the question wrongly as I'm still quite new to C++. 来源： https://stackoverflow.com/questions/37151476/how-to-use

I'm lemmatizing the Ted Dataset Transcript. There's something strange I notice: Not all words are being lemmatized. To say, selected -> select Which is right. However, involved !-> involve and horsing !-> horse unless I explicitly input the 'v' (Verb) attribute. On the python terminal, I get the right output but not in my code : >>> from nltk.stem import WordNetLemmatizer >>> from nltk.corpus import wordnet >>> lem = WordNetLemmatizer() >>> lem.lemmatize('involved','v') u'involve' >>> lem.lemmatize('horsing','v') u'horse' The relevant section of the code is this: for l in LDA_Row[0].split('+')

I would like to use the wordnet lemmatizer to lemmatize the words in a > a<-c("He saw a see-saw on a sea shore", "she is feeling cold") > a [1] "He saw a see-saw on a sea shore" "she is feeling cold" I convert a into a corpus and do pre-processing steps (like stopword removal, lemmatization etc) > a <- Corpus(VectorSource(a)) I wanted to do the lemmatization in the below way, > filter <- getTermFilter("ExactMatchFilter", a, TRUE) > terms <- getIndexTerms("NOUN", 1, filter) > sapply(terms, getLemma) but I get this error > filter <- getTermFilter("ExactMatchFilter", a, TRUE) Error in .jnew(paste

I am new to spacy and I want to use its lemmatizer function, but I don't know how to use it, like I into strings of word, which will return the string with the basic form the words. Examples: 'words'=> 'word' 'did' => 'do' Thank you. damio Previous answer is convoluted and can't be edited, so here's a more conventional one. # make sure your downloaded the english model with "python -m spacy download en" import spacy nlp = spacy.load('en') doc = nlp(u"Apples and oranges are similar. Boots and hippos aren't.") for token in doc: print(token, token.lemma, token.lemma_) Output: Apples 6617 apples