Only articles of sufficient length are returned (short articles & redirects If you have any questions or suggestions, please drop a line in the comments section.Iterate over the dump, returning text version of each article as a list Print ('Input query : '.format(score, result)) Below we a select a random document from the document corpus and find documents similar to it.ĭocsim_index = SoftCosineSimilarity.load('./models/gensim_docims_index') To use the docsim index, we load the index matrix and search a query string against the index to find the most similar documents. The index matrix can be saved to the diskĭocsim_index = SoftCosineSimilarity(bow_corpus, similarity_matrix, num_best=10)ĭocsim_index.save('./models/gensim_docims_index') Next we compute soft cosine similarity against a corpus of documents by storing the index matrix in memory. Similarity_matrix = SparseTermSimilarityMatrix(termsim_index, dictionary) Using the document corpus we construct a dictionary, and a term similarity matrix.Ĭorpus_list_token = ĭictionary = Dictionary(corpus_list_token)īow_corpus = Termsim_index = WordEmbeddingSimilarityIndex(gates_model.wv) Using the Word2vec model we build WordEmbeddingSimilarityIndex model which is a term similarity index that computes cosine similarities between word embeddings.
But in this case we use it together with the word2vec that we build with a larger corpusĭoc_df = pd.read_json('./data/document_data.json') If this document corpus is large we can directly use it to build the Doc2Vec solution. We then load the document corpus for which we need to build the document similarity functionality. Gates_model = Word2Vec.load('./models/corpus_word2vec.model') We have all the pieces in place, let’s begin by loading the word2vec model It is not necessary to build the word2vec model with our own corpus, in case you do not have a sufficiently large corpus to build a word2vec model you can use an off-the-shelf model.įrom gensim.models import Word2Vec, WordEmbeddingSimilarityIndexįrom gensim.similarities import SoftCosineSimilarity, SparseTermSimilarityMatrixīelow is a simple preprocessor to clean the document corpus for the document similarity use-caseĭoc = word_tokenize(doc) # Tokenize to wordsĭoc = # Remove stopwords.ĭoc = # Remove numbers and special characters Model.save('./models/corpus_word2vec.model') ain(corpus_sentences, total_examples=rpus_count, epochs=er)
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This iterator code is from gensim word2vec tutorialįor line in open(os.path.join(self.dirname, fname), encoding='cp1252'):īelow code iterates over corpus sentences and creates a word2vec model and saves it to the diskĬorpus_sentences = MySentences('./data/corpus/') Gensim requires that the input must provide sentences sequentially, when iterated over.īelow is a small iterator which can process the input file by file, line by line.
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I have a large corpus of sentences extracted from windows txt files stored as sentences one per line in a single folder.
Let’s begin my importing the needed packages The traditional cosine similarity considers the vector space model (VSM) features as independent or orthogonal, while the soft cosine measure proposes considering the similarity of features in VSM, which help generalize the concept of cosine (and soft cosine) as well as the idea of (soft) similarity.
The solution is based SoftCosineSimilarity, which is a soft cosine or (“soft” similarity) between two vectors, proposed in this paper, considers similarities between pairs of features. In the blog, I show a solution which uses a Word2Vec built on a much larger corpus for implementing a document similarity. In many cases, the corpus in which we want to identify similar documents to a given query document may not be large enough to build a Doc2Vec model which can identify the semantic relationships among the corpus vocabulary. One problem with that solution was that a large document corpus is needed to build the Doc2Vec model to get good results. In a previous blog, I posted a solution for document similarity using gensim doc2vec.
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