information-retrieval

I want to know what can be used to determine the relevance of a page for a theme like games, movies, etc. Is there some research in this area or is there only counting how many times some relevant words appear? The common choice is supervised document classification on bag of words (or bag of n-grams) features, preferably with tf-idf weighting. Popular algorithms include Naive Bayes and (linear) SVMs. For this approach, you'll need labeled training data, i.e. documents annotated with relevant themes. See, e.g., Introduction to Information Retrieval , chapters 13-15. 来源： https://stackoverflow

I want to make a program that will retrieve some information a url. For example i give the url below, from librarything How can i retrieve all the words below the "TAGS" tab, like Black Library fantasy Thanquol & Boneripper Thanquol and Bone Ripper Warhammer ? I am thinking of using java, and design a data mining wrapper, but i am not sure how to start. Can anyone give me some advice? EDIT: You gave me excellent help, but I want to ask something else. For every tag we can see how many times each tag has been used, when we press the "number" button. How can I retrieve that number also? You

Closed . This question is opinion-based . It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post . Closed 6 years ago . My aim is to build an aggregrator of news feeds and blog feeds so as to make searching/tracking of entitites in it easy. I have been looking at many solutions out there like Terrier, Lucene, SWISH-E, etc. Basically, I could find only 2 sources of comparison studies done on these engines and one of them is kinda outdated. Basically I want a search engine which would be used

So, I am working on a Wikipedia dump to compute the pageranks of around 5,700,000 pages give or take. The files are preprocessed and hence are not in XML. They are taken from http://haselgrove.id.au/wikipedia.htm and the format is: from_page(1): to(12) to(13) to(14).. from_page(2): to(21) to(22).. . . . from_page(5,700,000): to(xy) to(xz) so on. So. basically it's a construction of a [5,700,000*5,700,000] matrix, which would just break my 4 gigs of RAM. Since, it is very-very Sparse, that makes it easier to store using scipy.lil.sparse or scipy.dok.sparse , now my issue is: How on earth do I

I wish to return ALL the Tweets I have ever posted on my timeline. I am using the Linq To Twitter library as so - var statusTweets = from tweet in twitterCtx.Status where tweet.Type == StatusType.User && tweet.UserID == MyUserID && tweet.Count == 200 select tweet; statusTweets.ToList().ForEach( tweet => Console.WriteLine( "Name: {0}, Tweet: {1}\n", tweet.User.Name, tweet.Text)); This works fine and brings back the first 200. However the first 200 seems to be the maximum I can retrieve, as there a way to bring back say 1000? There does not seem to be a next cursor movement option like there is

OCR generated texts sometimes come with artifacts, such as this one: Diese grundsätzliche V e r b o r g e n h e i t Gottes, die sich n u r dem N a c h f o l g e r ö f f n e t , ist m i t d e m Messiasgeheimnis gemeint While it is not unusual, that the spacing between letters is used as emphasis (probably due to early printing press limitations), it is unfavorable for retrieval tasks. How can one turn the above text into a more, say, canonical form, like: Diese grundsätzliche Verborgenheit Gottes, die sich nur dem Nachfolger öffnet, ist mit dem Messiasgeheimnis gemeint Can this be done