Defining answer classes using resolution refutation

TL;DR: Modify the skolem arguments to broaden the notion of answer literal to all context of question that conducted, including universal quantifier and ground term, to implement proposed logical formalisms by expanding the notionof answer literal for understanding task such as question answering.

TL;DR: Inference graphs are presented, a natural deduction inference system which scales well on multi-core and multi-processor machines and shows significant speedup in both best case and worst case inference scenarios as the number of processors increases.

TL;DR: A question understanding model is proposed to understand and process syntactic and semantic structure of questions and could more perfectly understand semantic content of questions than using pure Chinese semantic analysis.

TL;DR: An algorithm is proposed which extracts a case-based answer to the exact same question, from a deduction of P(T1)∩P(T2)∨ . . . p(Tk), k ≥ 2, on the form of a proof-tree, which specifies the cases in which each element P(Ti) is true.

TL;DR: In this paper, the authors explored the hypothesis that the members of categories which are considered most prototypical are those with most attributes in common with other members of the category and least attributes with other categories and found that family resemblance offers an alternative to criterial features in defining categories.

TL;DR: The paper concludes with a discussion of several principles which are applicable to the design of efficient proof-procedures employing resolution as the basle logical process.

TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.