Search Results for "frequency"

OCR Manga Reader is a free and open source Android app that allows you to quickly OCR and lookup Japanese words in real-time. It does not have ads or telemetry/spyware and does not require an Internet connection. Supports both EDICT and EPWING dictionaries. Requires Android 4.0 (Ice Cream Sandwich) or higher. See http://ocrmangareaderforandroid.sourceforge.net/ for details.

...DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP full text search engine (http://cwb.sourceforge.net) and a range of statistical functions (factorial analysis, classification, cooccurrency analysis, etc.) based on R packages (http://www.r-project.org). Read the scientific background at the Textométrie project web site http://textometrie.ens-lyon.fr/?...

TF-IDF.jar is a Java Archive file to measure TF-IDF of each document in a document collection (corpus). The jar can be used to (a) get all the terms in the corpus (b) get the document frequency (DF) and inverse document frequency (IDF) of all the terms in the corpus (c) get the TF-IDF of each document in the corpus (d) get each term with their frequency (no. of presence), term frequency (TF) and TF-IDF in every document

The TextProcessor Java package is a text processing toolkit, which provides some frequently used text processing functions such as stemming, removing stop-words, generating a term vocabulary, and calculating the term-doc frequency matrix. Basic topic mining models such as LDA and sparse NMF are also supported. The package can also generate feature files from a given text dataset with LDA and LIBSVM format for posterior procedures such as classification or clustering. The toolkit is also being extended for more advanced text analysis tasks based on natural language processing techniques.

Java Suffix array library for phrase discovery. Inspired initially by the classic paper of Yamamoto & Church, with newer ideas from Abouelhoda et al and Kim et al. Adapted for large alphabet so that words can be tokenized as alphabet characters.