Logic Programs vs. First-Order Formulas in Textual Inference

In the problem of recognizing textual entailment, the goal is to decide, given a text and a hypothesis expressed in a natural language, whether a human reasoner would call the hypothesis a consequence of the text. One approach to this problem is to use a first-order reasoning tool to check whether the hypothesis can be derived from the text conjoined with relevant background knowledge, after expressing all of them by first-order formulas. Another possibility is to express the hypothesis, the text, and the background knowledge in a logic programming language, and use a logic programming system. We discuss the relation of these methods to each other and to the class of effectively propositional reasoning problems. This leads us to general conclusions regarding the relationship between classical logic and answer set programming as knowledge representation formalisms

Using answer set programming in an inference-based approach to natural language semantics

The traditional tri-partition syntax/semantics/pragmatics is commonly used in most of the computer systems that aim at the simulation of the human understanding of Natural Language (NL). This conception does not reflect the flexible and creative manner that humans use in reality to interpret texts. Generally speaking, formal NL semantics is referential i.e. it assumes that it is possible to create a static discourse universe and to equate the objects of this universe to the (static) meanings of words. The meaning of a sentence is then built from the meanings of the words in a compositional process and the semantic interpretation of a sentence is reduced to its logical interpretation based on the truth conditions. The very difficult task of adapting the meaning of a sentence to its context is often left to the pragmatic level, and this task requires to use a huge amount of common sense knowledge about the domain. This approach is seriously challenged (see for example [4][14]). It has been showed that the above tri-partition is very artificial because linguistic as well as extra-linguistic knowledge interact in the same global process to provide the necessary elements for understanding. Linguistic phenomena such as polysemy, plurals, metaphors and shifts in meaning create real difficulties to the referential approach of the NL semantics discussed above. As an alternative solution to these problems, [4] proposes an inferential approac