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. 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. However for a computer these are completely different making spotting these nearly identical strings difficult.
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. The most common variant is looking over edge detection areas of the same or similar brightness. Vectorization of raster images is done by converting pixel color information into simple geometric objects. Convert raster images like PNGs, BMPs and JPEGs to scalable vector graphics (SVG, EPS, DXF) Upload Images How does it work.
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