An Intent may comprise a group of two or more words that have semantic meaning together or that represent a cluster or group of texts that share the same semantic meaning, e.g., the reason why the user has written a certain text or the core of that text. 

Some examples of Intents are those that are topic-related (e.g., bag-check), action related (e.g., Pay-bill, fix-please), sentiment related (e.g., great-service), emotion related (e.g., Hate-airline), characteristic-related (e.g., Gluten-free, battery-life), brand related (e.g., Trader Joe’s-quality), and product related (e.g., Iphone-x), but others may be utilized.
​

The algorithm that does the Intent Induction is based on measures from Information Theory. Based on the relevance of semantic representations in the whole corpora, the algorithm is able to extract which are the most representative and meaningful contexts using entropy, information gain and divergence measures.

Intents don’t have to be bigrams but bigrams are usually a way of contexts disambiguation. Eg “customer” may be ambiguous but “customer+service” is not.

Intents that are more specific have different semantic variations than other Intents. For example, the Intent password→incorrect is more specific since the dataset has a different semantic variation of password that includes the word incorrect.

Intents that are more specific do not have to be unigrams. More than one word can be used to make an Intent more specific in some cases. For example, the Intent need+help uses send+email to make the Intent more specific.

An Intent may comprise a group of two or more words that have semantic meaning together or that represent a cluster or group of texts that share the same semantic meaning. We define the name using that group of words.

Once an Intent, and therefore the semantic relationship between a pair of words or more, is understood by a system of the present invention, such a system can identify similar contexts automatically even if the group of words comprising the Intent are not included in the similar context. By way of example, an inquiry made as to whether a passenger may “take” a “briefcase” onboard may be related to a “bag-check” Intent.

Some Intents can be more specific than other Intents if the dataset has an Intent with many semantic variations.