That's worthy provided that you must look for the complete sentence “I am from Bangkok. ”, yet now not with the intention to look for simply “Bangkok. ” GET /_analyze? tokenizer=icu_tokenizer สวัสดี ผมมาจากกรุงเทพฯ The icu_tokenizer, nevertheless, is ready to get a divorce the textual content into the person phrases (สวัสดี, ผม, มา, จาก, กรุงเทพฯ), making them more uncomplicated to go looking. against this, the normal tokenizer “over-tokenizes” chinese language and eastern textual content, frequently breaking apart complete phrases into unmarried characters. simply because there are not any areas among phrases, it may be tricky to inform even if consecutive characters are separate phrases or shape a unmarried observe. for example: 向 potential dealing with, 日 capability solar, and 葵 ability hollyhock. whilst written jointly, 向日葵 skill sunflower. 五 capability 5 or 5th, 月 skill month, and 雨 skill rain. the 1st characters written jointly as 五月 suggest the month of may perhaps, and including the 3rd personality, 五月雨 ability non-stop rain. whilst mixed with a fourth personality, 式, that means sort, the be aware 五月雨式 turns into an adjective for something consecutive or unrelenting. even supposing each one personality could be a observe in its personal correct, tokens are extra significant once they keep the larger unique proposal rather than simply the part components: GET /_analyze? tokenizer=standard 向日葵 GET /_analyze? tokenizer=icu_tokenizer 向日葵 the normal tokenizer within the previous instance might emit each one personality as a separate token: 向, 日, 葵. The icu_tokenizer might emit the one token 向日葵 (sunflower). one other distinction among the normal tokenizer and the icu_tokenizer is that the latter will holiday a notice containing characters written in several scripts (for instance, βeta) into separate tokens—β, eta—while the previous will emit the be aware as a unmarried token: βeta. Tidying Up enter textual content Tokenizers produce the simplest effects whilst the enter textual content is fresh, legitimate textual content, the place legitimate implies that it follows the punctuation ideas that the Unicode set of rules expects. commonly, even though, the textual content we have to technique is something yet fresh. cleansing it up prior to tokenization improves the standard of the output. Tokenizing HTML Passing HTML throughout the commonplace tokenizer or the icu_tokenizer produces bad effects. those tokenizers simply don’t be aware of what to do with the HTML tags. for instance: GET /_analyzer? tokenizer=standard

Some déjà vu website the normal tokenizer confuses HTML tags and entities, and emits the subsequent tokens: p, a few, d, eacute, j, agrave, vu, a, href, http, somedomain. com, site, a. basically now not what used to be meant! personality filters may be extra to an analyzer to preprocess the textual content prior to it's handed to the tokenizer. for that reason, we will use the html_strip personality filter out to take away HTML tags and to decode HTML entities corresponding to é into the corresponding Unicode characters. personality filters should be verified out through the examine API by way of specifying them within the question string: GET /_analyzer? tokenizer=standard&char_filters=html_strip

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