Sentence examples for generates sentences from inspiring English sources

Although many variations of dummy text exist (a recent addition being "Hipster Ipsum," which generates sentences like "Single-origin coffee four loko fap dreamcatcher hoodie, carles wayfarers vice bicycle rights synth keytar brooklyn"), "Lorem Ipsum" (which translates roughly as "suffering itself") still remains the industry standard.

Something so commonplace it barely has life beyond the knee-jerk "thieving BASTARDS" response it elicits, but ends up being a top BBC News story with interviews with three separate people because it generates sentences like "Unfortunately, Santa parked his sleigh in the wrong bay" and "Santa was fuming".

Automatically generated sentences were created at random using the sentence generator.

The generated sentences make grammatical sense, maintain a sort of theme and for the most part fit with the start and end sentence.

The results show interesting improvements in the ability of the machine to generate sentences that make sense together, which could led to much more human-like interactions with AI chatbots, perhaps even Google's Now.

With all the text that flows through Twitter every day, he said, "It's sort of the perfect fodder for the bot that learns and turns it into its own generated sentences".

Some of the algorithmically generated sentences Virtual Joey came up with include "Hey Ross do you want me to talk to some lady" and "I like pizza with cheese".

Buonomano describes a famous experiment by the psychologist John Bargh and colleagues in which subjects who unscrambled lists of words that yielded sentences about politeness, like "They usually encourage her," were later more polite toward a lab assistant than were subjects who generated sentences related to rudeness.

The latter is a sentence in the language, whereas the former is just a recipe for generating sentences.

This paper presents a novel approach to learning of relations among motions, objects, and language, and to generating sentences that describe human actions.

This requires, for example, the ability to accept or generate sentences wherein verb arguments appear in positions different from those seen in the training set.