wordnet

I'm having trouble running wordnet in R . I loaded it into the library initially, but it didn't work. The error looked like this: Warning message: In initDict() : cannot find WordNet 'dict' directory: please set the environment variable WNHOME to its parent So, I added this line: Sys.setenv(WNHOME = "C:\\Program Files (x86)\\WordNet\\2.1") and then was able to use the library function to load it. I don't understand this line or error message at all, but it seems to fix this problem. However, whenever I try to use the package, it won't work. For example, I entered: filter <- getTermFilter(

I just downloaded a Spanish Wordnet from the project GRIAL , the format is XML. How can I use it in Python NLTK? Besides that, in the same page you can download a tagged corpus in Spanish. How can I incorporate it as well? Use XMLCorpusReader to load XML data as corpus Here's the code to do that from nltk.corpus.reader import XMLCorpusReader reader = XMLCorpusReader(dir, file) A fully working example which uses XMLCorpusReader is given here 来源： https://stackoverflow.com/questions/25615741/how-to-use-the-spanish-wordnet-in-nltk

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 came across several methods for measuring semantic similarity that use the structure and hierarchy of WordNet, e.g. Jiang and Conrath measure (JNC), Resnik measure(RES), Lin measure (LIN) etc. The way they are measured using NLTK is: sim2=wn.jcn_similarity(entry1,entry2,brown_ic) sim3=entry1.res_similarity(entry2, brown_ic) sim4=entry1.lin_similarity(entry2,brown_ic) If WordNet is the basis of calculating semantic similarity, what is the use of Brown Corpus here? arturomp Take a look at the explanation at the NLTK howto for wordnet. Specifically, the *_ic notation is information content .