Language Modeling with Utterance-Meaning Pairs

In this paper, we develop an argument from behavioristic psychology leading to the definition of a mathematical object which we call utterance-meaning pair. With this concept, the combination of a language model with a method to associate one or more meanings to an utterance can be mathematically described as set of utterance-meaning pairs

Machine Semiotics

Despite their satisfactory speech recognition capabilities, current speech assistive devices still lack suitable automatic semantic analysis capabilities as well as useful representation of pragmatic world knowledge. Instead, current technologies require users to learn keywords necessary to effectively operate and work with a machine. Such a machine-centered approach can be frustrating for users. However, recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome this shortcoming: For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the meanings of individual words, nor the meaning of phrasal and sentence semantics that combine individual word meanings with additional implicit world knowledge. For speech assistive devices, the learning of machine specific meanings of human utterances by trial and error should be sufficient. Using the trivial example of a cognitive heating device, we show that -- based on dynamic semantics -- this process can be formalized as the learning of utterance-meaning pairs (UMP). This is followed by a detailed semiotic contextualization of the previously generated signs.Comment: 37 pages, 4 table

Woher kommen die p-Normen?

Kurze Notiz zum Ursprung der p-Normen bei Minkowski und Riesz