document-classification

I have X as a csr_matrix that I obtained using scikit's tfidf vectorizer, and y which is an array My plan is to create features using LDA, however, I failed to find how to initialize a gensim's corpus variable with X as a csr_matrix. In other words, I don't want to download a corpus as shown in gensim's documentation nor convert X to a dense matrix, since it would consume a lot of memory and the computer could hang. In short, my questions are the following, How do you initialize a gensim corpus given that I have a csr_matrix (sparse) representing the whole corpus? How do you use LDA to extract

I know WordNet has Domains Hierarchy: e.g. sport->football. 1) Is it possible to list all words related, for example, to the 'sport->football' sub-domain? Response: goalkeeper, forward, penalty, ball, field, stadium, referee and so on. 2) Get domain's name for a given word , e.g. 'goalkeeper'? Need something like [sport->football; sport->hockey] or [football;hockey] or just 'football'. It is for a document classification task. WordNet has a hypernym / hyponym hierarchy but that is not what you want here, as you can see when you look up goalkeeper : from nltk.corpus import wordnet s = wordnet

Pretty common situation, I'd wager. You have a blog or news site and you have plenty of articles or blags or whatever you call them, and you want to, at the bottom of each, suggest others that seem to be related. Let's assume very little metadata about each item. That is, no tags, categories. Treat as one big blob of text, including the title and author name. How do you go about finding the possibly related documents? I'm rather interested in the actual algorithm, not ready solutions, although I'd be ok with taking a look at something implemented in ruby or python, or relying on mysql or pgsql