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To calculate the similarity between two vectors of TF-IDF values the Cosine Similarity is usually used. The last term (‘INC’) has a relatively low value, which makes sense as this term will appear often in the corpus, thus receiving a lower IDF weight. You can see the first row (“!J INC”) contains three terms for the columns 11, 16196, and 15541. Scikit-learn deals with this nicely by returning a sparse CSR matrix. The resulting matrix is very sparse as most terms in the corpus will not appear in most company names.
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The code to generate the matrix of TF-IDF values for each is shown below. In this case there where some company names ending with “ BD” that where being identified as similar, even though the rest of the string was not similar. This is a nice example of one of the pitfalls of this approach: some terms that appear very infrequent will result in a high bias towards this term. Next to removing some punctuation (dots, comma’s etc) it removes the string “ BD”. The following function cleans a string and generates all n-grams in this string:Īs you can see, the code above does some cleaning as well. This is why we will use n-grams: sequences of N contiguous items, in this case characters. In our case using words as terms wouldn’t help us much, as most company names only contain one or two words. While the terms in TF-IDF are usually words, this is not a necessity. It is used to transform documents into numeric vectors, that can easily be compared. TF-IDF is very useful in text classification and text clustering. Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12. Then, the inverse document frequency (i.e., idf) is calculated as log(10,000,000 / 1,000) = 4. Now, assume we have 10 million documents and the word cat appears in one thousand of these. The term frequency (i.e., tf) for cat is then (3 / 100) = 0.03. IDF(t) = log_e(Total number of documents / Number of documents with term t in it).Īn example (from Consider a document containing 100 words in which the word cat appears 3 times. the, it, and etc) down, and words that don’t occur frequently up. This last term weights less important words (e.g. TF-IDF is a method to generate features from text by multiplying the frequency of a term (usually a word) in a document (the Term Frequency, or TF) by the importance (the Inverse Document Frequency or IDF) of the same term in an entire corpus. I don’t know anything about the data or the amount of duplicates in this dataset (it should be 0), but most likely there will be some very similar names. It contains all company names in the SEC EDGAR database. I just grabbed a random dataset with lots of company names from Kaggle. Super Fast String Matching in Python Traditional approaches to string matching such as the Jaro-Winkler or Levenshtein distance measure are too slow for large datasets. In this post I will explain how this can be done faster using TF-IDF, N-Grams, and sparse matrix multiplication. Every entry has to be compared with every other entry in the dataset, in our case this means calculating one of these measures 663.000^2 times. The obvious problem here is that the amount of calculations necessary grow quadratic. One way to solve this would be using a string similarity measures like Jaro-Winkler or the Levenshtein distance measure.
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The following table gives an example: Company Nameįor the human reader it is obvious that both Mc Donalds and Mac Donald’s are the same company. A similar problem occurs when you want to merge or join databases using the names as identifier. This is a problem, and you want to de-duplicate these. Databases often have multiple entries that relate to the same entity, for example a person or company, where one entry has a slightly different spelling then the other. Update: run all code in the below post with one line using string_grouper: Name MatchingĪ problem that I have witnessed working with databases, and I think many other people with me, is name matching. Using this approach made it possible to search for near duplicates in a set of 663,000 company names in 42 minutes using only a dual-core laptop. Using TF-IDF with N-Grams as terms to find similar strings transforms the problem into a matrix multiplication problem, which is computationally much cheaper. Traditional approaches to string matching such as the Jaro-Winkler or Levenshtein distance measure are too slow for large datasets.