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Excerpt from Directions in Artificial Intelligence: Natural Language Processing
Most recently, a paper that hasn't gotten around very much, is a very ambitious effort by Roger Schank [9] on setting up discourse structure for paragraphs. That paper will probably be circulating pretty widely by Spring. In that, he very ambitiously takes the folk tale from Eskimo literature several paragraph stories about 5 I think and he makes a causal chain of the relationships among the deep conceptual dependency structures that he derives. It is easily criticizable. There are many things where each of us would differ on whether that is the path or exactly what is going on. But I admire it very much because it is a very ambitious attempt to deal with a fairly large piece of text. The point is we don't have models for dealing with large amounts of text, or even with several paragraphs of text. We don't really know how to handle the semantics of text discourse.
The basic problems are: How do you use sentences to understand following sentences? How do you solve problems of reference? And what is an adequate lexical structure to do this? What is an adequate semantic structure to represent the resulting meanings? That whole family of questions needs to be answered.
We are very much concerned in my group at Texas with studying all these things. We have one student (we don't have a big budget for this) but one student is working on discourse analysis a dissertation by Hendrix [10] is about finished on modelling techniques an approach toward representing meanings as set theoretic expressions. It takes semantic nets right down to the abstract algebraic description of what the meaning of sentences is. And what I will talk about today is Robert Amsler's studies of the Merriam - Webster dictionaries.
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Paperback. Condition: New. Print on Demand. This book examines semantic word classification through computational linguistics by analyzing real-world examples from scientific fields. The author, an expert in computational linguistics, presents a methodology for extracting semantic classes from scientific subfields and organizing them into a hierarchical structure. This innovative approach reveals the underlying patterns and relationships within specialized vocabularies, offering valuable insights into the semantics of scientific discourse. The book highlights the importance of structuring natural language data for effective information processing and retrieval, a crucial aspect in fields such as question-answering systems and scientific literature analysis. By providing a framework for understanding the organization of scientific language, this book contributes to the advancement of natural language processing and its applications in various domains. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Seller Inventory # 9781330202890_0
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