Countvectorizer has feature names, like this. What would be the feature names for a glove vector? How to get those feature names? I have the glove vector file of 300 dimensions like the above shown. What would be the name of the 300 dimensions of the glove vectors? Answer There is no name for the Glove features. The countvectorizer counts the occurrences of each token in each sentence. So the features have easily understandable names. The feature “cat” is the count in each sentence of the token “cat”. For Glove Vectors, the strategy is totally different and there is no
I have an list of sentences like this: And i need that list like this: Answer You want to convert all elements of a list into a single string right ? This might help you. it will give you a single string variable
I would like how to convert the first letter of each word in this column: Into lower case, in order to have I know there is capitalize() but I would need a function which does the opposite. Many thanks Please note that the strings are within a column. Answer I don’t believe there is a builtin for this, but I could be mistaken. This is however quite easy to do with string comprehension!. Where line is each individual line.
I am using the below “fastest” way of removing punctuation from a string: However, it removes all punctuation including apostrophes from tokens such as shouldn’t turning it into shouldnt. The problem is I am using NLTK library for stopwords and the standard stopwords don’t include such examples without apostrophes but instead have tokens that NLTK would generate if I used the NLTK tokenizer to split my text. For example for shouldnt the stopwords included are shouldn, shouldn’t, t. I can either add the additional stopwords or remove the apostrophes from the NLTK stopwords. But both solutions don’t seem “correct” in
spaCy tags up each of the Tokens in a Document with a part of speech (in two different formats, one stored in the pos and pos_ properties of the Token and the other stored in the tag and tag_ properties) and a syntactic dependency to its .head token (stored in the dep and dep_ properties). Some of these tags are self-explanatory, even to somebody like me without a linguistics background: Others… are not: Worse, the official docs don’t contain even a list of the possible tags for most of these properties, nor the meanings of any of them. They sometimes