An eclectic IT career: Mayo Takeuchi

Having introduced the "prior art" approaches of identifying textual language in part 1 and part 3 (to wit: stop word presence and n-gram detection), I can now speak to the patented idea which we implemented as part of  LanguageWare , which is a set of Java libraries that offer NLP functionality. Simply put, our solution involves a  dictionary that is highly compactible (I may ask a guest blogger from my former team to delve into this aspect), and thus made it possible to store the following types of information: Each entry consists of the following: Term or n-gram Language(s) with which it's associated Whether it can occur as a standalone term, at the beginning of a word, the middle of a word, or the end of a word, or some combination of these, and An integer weighting value (per term/language pairing) Thus, for the Chinese Simplified/Traditional and Japanese disambiguation problem, the Japanese-specific kana (listed as unigrams) were given large positive va

Word cloud of one person's compilation of English stop words, courtesy of Armand Brahaj (whose site has been infected by malware). Here instead is ranks.nl's list   Now that half a year has lapsed since the inception of this blog, some readers may be wondering when I might share more topics that are related to the "Linguistics" part of "SEO, Linguistics, Localization". In fact, one of the triggers of my instigating this blog arose from the issuance of two patents, which had been filed in 2005 and 2006 for which I was a co-inventor and sole inventor , respectively. Both filings concerned language identification (from textual input): the first approached the challenges of identifying a text's (primary) language, and the second was an application of the first, as combined with messaging software. Rather than overwhelm the reader with extensive explanations, I'm going to attempt to create a series of posts that will cover everything in the way