Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. Query understanding is at the heart of technologies like Amazon Alexa, Apple's Siri. Google Assistant, IBM's Watson, and Microsoft's Cortana. Methods Tokenization Tokenization is the process of breaking up a text string into words or other meaningful elements called tokens. Typically, tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a "word". Often a tokenizer relies on simple heuristics, such as splitting the string on punctuation and whitespace characters. Tokenization is more challenging in languages without spaces between words, such as Chinese and Japanese. Tokenizing text in these languages requires the use of word segmentation algorithms. Spelling correction Spelling correction is the process of automatically detecting and correcting spelling errors in search queries. Most spelling correction algorithms are based on a language model, which determines the a priori probability of an intended query, and an error model (typically a noisy channel model), which determines the probability of a particular misspelling, given an intended query. Stemming and lemmatization Many, but not all, languages inflect words to reflect their role in the utterance they appear in: a word such as *care* may appear as, besides the base form. as *cares*, *cared*, *caring*, and others. The variation between various forms of a word is likely to be of little importance for the relatively coarse-grained model of meaning involved in a retrieval system, and for this reason the task of conflating the various forms of a word is a potentially useful technique to increase recall of a retrieval system. The languages of the world vary in how much morphological variation they exhibit, and for some languages there are simple methods to reduce a word in query to its lemma or root form or its stem. For some other languages, this operation involves non-trivial string processing. A noun in English typically appears in four variants: *cat* *cat's* *cats* *cats'* or *child* *child´s* *children* *children's*. Other languages have more variation. Finnish, e.g., potentially exhibits about 5000 forms for a noun, and for many languages the inflectional forms are not limited to affixes but change the core