Hyperintensional Reasoning based on Natural Language Knowledge Base
The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy's Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Monads for hyperintensionality? A situation semantics for hyperintensional side effects
We outline a hyperintensional situation semantics in which hyperintensionality is modelled as a {`}side effect{'}, as this term has been understood in natural language semantics and in functional programming. We use mona…
Language Models as Inductive Reasoners
Inductive reasoning is a core component of human intelligence. In the past research of inductive reasoning within computer science, formal language is used as representations of knowledge (facts and rules, more specifica…
PhilosophyLogical Reasoning over Natural Language as Knowledge Representation: A Survey
Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses formal language as knowledge representation a…
Logical ReasoningSurveyDoes External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in …
Natural Language InferenceExBERT: An External Knowledge Enhanced BERT for Natural Language Inference
Neural language representation models such as BERT, pre-trained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasonin…
Knowledge GraphsNatural Language Inference