import nltk is Python syntax, and as such won’t work in a shell script. To test the version of nltk and scikit_learn, you can write a Python script and run it. Such a script may look like import nltk import sklearn print(‘The nltk version is {}.’.format(nltk.__version__)) print(‘The scikit-learn version is {}.’.format(sklearn.__version__)) # The nltk version … Read more
from nltk.corpus import stopwords # … filtered_words = [word for word in word_list if word not in stopwords.words(‘english’)]
Take a look at the other tokenizing options that nltk provides here. For example, you can define a tokenizer that picks out sequences of alphanumeric characters as tokens and drops everything else: from nltk.tokenize import RegexpTokenizer tokenizer = RegexpTokenizer(r’\w+’) tokenizer.tokenize(‘Eighty-seven miles to go, yet. Onward!’) Output: [‘Eighty’, ‘seven’, ‘miles’, ‘to’, ‘go’, ‘yet’, ‘Onward’]
Great native python based answers given by other users. But here’s the nltk approach (just in case, the OP gets penalized for reinventing what’s already existing in the nltk library). There is an ngram module that people seldom use in nltk. It’s not because it’s hard to read ngrams, but training a model base on … Read more
To save some folks some time, here is a list I extracted from a small corpus. I do not know if it is complete, but it should have most (if not all) of the help definitions from upenn_tagset… CC: conjunction, coordinating & ‘n and both but either et for less minus neither nor or plus … Read more
I had this same problem. Go into a python shell and type: >>> import nltk >>> nltk.download() Then an installation window appears. Go to the ‘Models’ tab and select ‘punkt’ from under the ‘Identifier’ column. Then click Download and it will install the necessary files. Then it should work!
For (much) more power and flexibility, use a dedicated spellchecking library like PyEnchant. There’s a tutorial, or you could just dive straight in: >>> import enchant >>> d = enchant.Dict(“en_US”) >>> d.check(“Hello”) True >>> d.check(“Helo”) False >>> d.suggest(“Helo”) [‘He lo’, ‘He-lo’, ‘Hello’, ‘Helot’, ‘Help’, ‘Halo’, ‘Hell’, ‘Held’, ‘Helm’, ‘Hero’, “He’ll”] >>> PyEnchant comes with a … Read more
Short and dense: http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html The goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form. However, the two words differ in their flavor. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope … Read more
I assume entropy was mentioned in the context of building decision trees. To illustrate, imagine the task of learning to classify first-names into male/female groups. That is given a list of names each labeled with either m or f, we want to learn a model that fits the data and can be used to predict … Read more