Representing Inferences and their Lexicalization
This work addresses the challenge of improving inference handling in natural language processing, but it appears incremental as it focuses on proving feasibility of a specific design.
The paper tackled the problem of marshaling inferences from background knowledge for deep natural language understanding by developing a computational architecture based on word meanings as entities, predications, and inferences, and implemented it on real text to prove feasibility.
We have recently begun a project to develop a more effective and efficient way to marshal inferences from background knowledge to facilitate deep natural language understanding. The meaning of a word is taken to be the entities, predications, presuppositions, and potential inferences that it adds to an ongoing situation. As words compose, the minimal model in the situation evolves to limit and direct inference. At this point we have developed our computational architecture and implemented it on real text. Our focus has been on proving the feasibility of our design.