"As for why I tell a lot of stories, there's a joke about that. There was once a man who had a computer, and he asked it, 'Do you compute that you will ever be able to think like a human being?' And after assorted grindings and beepings, a slip of paper came out of the computer that said, 'That reminds me of a story . . . "
– from ANGELS FEAR: TOWARDS AN EPISTEMOLOGY OF THE SACRED. Gregory Bateson & Mary Catherine Bateson. (Part III 'Metalogue').
Narrative Science, a leader in Advanced Natural Language Generation (Advanced NLG) for the enterprise, announced the availability of its third annual research report, "Outlook on Artificial Intelligence in the Enterprise 2018." In partnership with the National Business Research Institute (NBRI), Narrative Science surveyed business executives from a wide array of functions, including business intelligence, finance, and product management, to understand the use, value, and impact of AI throughout their businesses. Narrative Science's analysis of the data revealed key findings, including the compelling discovery that almost two-thirds of enterprises utilized AI in 2017.
CHICAGO, Jan. 17, 2018 (GLOBE NEWSWIRE) -- Narrative Science, the leader in Advanced Natural Language Generation (Advanced NLG) for the enterprise, today announced the availability of its third annual research report, "Outlook on Artificial Intelligence in the Enterprise 2018." In partnership with the National Business Research Institute (NBRI), Narrative Science surveyed business executives from a wide array of functions, including business intelligence, finance, and product management, to understand the use, value, and impact of AI throughout their businesses. Narrative Science's analysis of the data revealed key findings, including the compelling discovery that almost two-thirds of enterprises utilized AI in 2017.
The Seventh International Workshop on Natural Language Generation was held from 21 to 24 June 1994 in Kennebunkport, Maine. Sixty-seven people from 13 countries attended this 4-day meeting on the study of natural language generation in computational linguistics and AI. The goal of the workshop was to introduce new, cuttingedge work to the community and provide an atmosphere in which discussion and exchange would flourish. Sixty-seven people from 13 countries attended this successful 4-day meeting, coming from as far away as Japan, Australia, and Europe. The study of language generation in computational linguistics and AI is still overshadowed by the study of parsing and analysis.
The PROSENET/TEXTNET approach is designed to facilitate the generation of polished prose by an expert system. The approach uses the augmented transition network (ATN) formalism to help structure prose generation at the phrase, sentence, and paragraph levels. The approach also uses expressive frames to help give the expert system builder considerable freedom to organize material flexibly at the paragraph level. The PROSENET /TEXTNET approach has been used in a number of prototype expert systems in medical domains, and has proved to be a convenient and powerful tool. One component of this interface for many systems involves the generation of English prose to communicate the expert system's conclusions and recommendations.
It was planned and coordinated by Kristiina Jokinen (Nara Institute of Science and Technology [NAIST]), Mark Maybury (The MITRE Corporation), Michael Zock (LIMSI-CNRS), and Ingrid Zukerman (Monash University). Thirty scholars from Europe, the United States, Australia, and Japan participated in the workshop. The purpose of the workshop was to clarify the role of rational and cooperative planning in generation in general and to bridge the gaps that seem to exist between theoretical models of planning agents and practical aspects of natural language generation (NLG) architecture. In recent years, there has been a focus shift in NLG from the study of well-formedness conditions (grammars) to the exploration of the communicative adequacy of linguistic forms: Speaking is viewed as an indirect means for achieving commupresentations, attempted to provide further material for building bridges. The workshop finished with a panel on the gaps and bridges theme, summarizing the topics of the ...
Text planning is one of the most rapidly growing subfields of language generation. Until the 1988 AAAI conference, no workshop has concentrated on text planning and its relationship to realization. This report is a summary of that workshop. Traditionally, systems that automatically generate natural language have been conceived as consisting of two principal components: a text planner and a realization grammar. Recent advances in the art, especially in the incorporation of generation systems into large computer applications, have prompted researchers to question this traditional categorization and the architectures used to embody generator systems.
These collocations are used by native speakers of a language almost without thought, yet they must be learned by nonnative speakers of the language. A native speaker of English might say that he/she drinks "strong coffee," but a nonnative speaker might say either "powerful coffee" or "sturdy coffee." Collocations tend to vary among languages and topic domains. Unfortunately, the task of correctly identifying lexical collocations, even by native speakers of the language, has been shown to be difficult. Computer systems that translate natural languages, or machine-translation systems, need to know about lexical collocation information to produce natural-sounding or colloquially proper text.