Language generations

It can be shown that the set of languages generated by context-free grammars is exactly equal to the set of languages accepted by pushdown automata [hopc79j. 

Colonies as well-formahzed language generating devices have been proposed in [24], and developed during the nineties in several directions in many papers [4,12,23,25,26,30,31,37,45,46]. 

In addition to its analytic capabilities, Watson also possesses an impressive capabihty to understand natural language. This capability will enable robust collaboration among providers and communication of information with all users. Bental and Cawsey (2002) use text analysis and natural language generation of medical record information, along with other techniques, to provide personalized and adaptive information to consumers. Text and data mining techniques are also described by Leroy et al. (2003). 

During the language generation session(s), sensory expert panelhsts blind taste every product of the set and determine which of the Sense references are needed to describe all the flavour characteristics of the product space. This step is at first individually performed in the sensory booths to enable an optimal focus from the sensory expert panelhsts. It is evenmally discussed by the team of sensory expert panellists, in order to achieve a consensual hst of descriptors, which will be reviewed and finalised in the training step. 

Flavourists are invited to attend the language generation step of QFPs and observe how sensory expert panellists build the Sense It descriptor list. They are strongly advised to smell or taste every Sense It reference that is discussed within the session. They may communicate questions to the sensory panel leader in charge of the sensory test, who will then check the answers with the sensory expert panellists. Indeed, they do not actively participate in the discussion, in order to prevent any bias of the sensory expert panellists perception. 

The involvement of the flavourists in the sensory language generation sessions brings benefit to both types of experts. The benefits to flavourists are listed first, followed by the benefits to the sensory experts. 

The process language generated by this QFG is the so-called Golden-Mean Process language [1]. The word distribution is shown in Fig. 2. It is characterized... 

The process languages generated by the spin-1 particle under a particular observation scheme can be analyzed using well known information-theoretic quantities such as Shannon block entropy and entropy rate [12] and others introduced in Ref. [25]. Here, we will limit ourselves to the excess entropy. The applicability of this analysis to quantum dynamical systems has been shown in Ref. [26], where closed-form expressions are given for some of these quantities when the generator is known. 

Nenkova, A., J. Chae, A. Louis, and E. Pitler. 2010. Structural features for predicting the linguistic quality of text Applications to machine translation, automatic summarization and human-authored text. Empirical Methods in Natural Language Generation, edited by E. Krahmer and M. Theune. Lecmre Notes in Artificial Intelligence, Vol. 5790. Berlin Springer-Verlag. pp. 222-241. 

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