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You have two mistakes in your loop, and also What you need is %in%: for (word in random_words) { if(! word %in% rain_words) { print(word) } } But a more R-style version would be this: > rain_words <- c( "cloud", "clouds", "drizzle", "hail", "mist", "monsoon", "rain", "...


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To add onto @Nicholas James Bailey's answer: tidytext provides functionality for two different main operations: text mining and text modeling. I think the text mining part of it where we tokenize, tidy and prep text data is a bit more unique. As pointed out there are several model alternatives for text data, some of which are arguably better. In terms of ...


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Individual feature selection methods assign a numerical value to every feature so that features can be ranked according to this value. The calculated value is chosen to represent how much the feature contributes to knowing the label/response variable: common choices are conditional entropy, but also information gain or correlation. The actual values assigned ...


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I would suggest you to use spacy and add your own custom labels for NER. https://spacy.io/usage/training/#ner


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Here is a great answer to this question. I'll summarize: The code example was taken from a "buggy" repository on GitHub and is not typical of robust solutions. Robust solutions actually do use the first word as a target word. If the context window is length 10, then the method uses the next 5 words as the context and the first word as the target (...


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