Gensim is a fairly mature package that has been used successfully by many individuals and companies, both for rapid prototyping and in production. See the Experiments on the English Wikipedia or perhaps check out Distributed Computing in gensim. To delve into more details, you can browse through the API Reference, Generalization is the reason why we apply transformations and do topic modellingĬongratulations, you have finished the tutorials – now you know how gensim works :-) Them sharing a “computer-human” related topic with the query. Which corresponds better to our intuition of Them received quite high similarity scores (no. However, after applying LSI, we can observe that both of 2 ( "The EPS user interface management system")Īnd 4 ( "Relation of user perceived response time to error measurement") would never be returned byĪ standard boolean fulltext search, because they do not share any common words with "Human computer interaction". The thing to note here is that documents no. 0.12416792 The generation of random binary unordered trees 0.10639259 The intersection graph of paths in trees 0.09879464 Graph minors IV Widths of trees and well quasi ordering No random-walk static ranks, just a semantic extension over the boolean keyword match:Ġ.9984453 The EPS user interface management systemĠ.998093 Human machine interface for lab abc computer applicationsĠ.9865886 System and human system engineering testing of EPSĠ.93748635 A survey of user opinion of computer system response timeĠ.90755945 Relation of user perceived response time to error measurement Similarities-on apparent semantic relatedness of their texts (words). Unlike modern search engines, here we only concentrate on a single aspect of possible Like to sort our nine corpus documents in decreasing order of relevance to this query. Now suppose a user typed in the query “Human computer interaction”. If you’re interested, you can read more about LSI here: Latent Semantic Indexing:
Our LSI space is two-dimensional ( num_topics = 2) so there are two topics, but this is arbitrary. Second, the benefit of LSI is that enables identifying patterns and relationships between terms (in our case, words in a document) and topics. LsiModel ( corpus, id2word = dictionary, num_topics = 2 )įor the purposes of this tutorial, there are only two things you need to know about LSI.įirst, it’s just another transformation: it transforms vectors from one space to another. Dictionary ( texts ) corpus = įrom gensim import models lsi = models. split ()) texts = for document in documents ] # remove words that appear only once frequency = defaultdict ( int ) for text in texts : for token in text : frequency += 1 texts = > 1 ] for text in texts ] dictionary = corpora. From collections import defaultdict from gensim import corpora documents = # remove common words and tokenize stoplist = set ( 'for a of the and to in'.
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