![]() ![]() The TF-IDF is a very popular information retrieval technique for free-text documents. Compute the cosine-similarity score between each pair of documents (returns a value between 0 and 1, where 0 means most different and 1 means most similar) Compute the TF-IDF weighting-scheme of the frequent-terms for each documentĥ. Profile each document in the index by extracting its frequent terms (usually modeled as a term vector where each term is accompanied with its frequency indicator inside the same document)Ĥ. Create a new Apache Lucene index for the documents you will search for similarityģ. In laymen’s terms, this translates to the following process:ġ. In Apache Lucene, there are supported capabilities to extract the descriptive metadata about the text-documents using frequent-terms extraction, then, for the second part of similarity computation, the project supports cosine-similarity computations between the TF-IDF terms frequencies (a popular information retrieval technique for free-text) for profiling the free-text documents. Both tasks can be handled by an open-source text-mining project like Apache Lucene. to compare the profiles of pairs of documents to detect their overall similarity. profile the documents to extract their descriptive metadata, 2. To handle the challenge of finding similar free-text documents, there is a need to apply a structured text-mining process to execute two tasks: 1. One of the main challenges in such Big Data environments is to find all similar documents which have common information. ![]() ![]() Nowadays, there are a lot of unstructured data available on the Internet, and more commonly, in Data Lakes (DL) specifically designed for Business Intelligence (BI). ![]()
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