HOW TO NOT MIX WORDS AND OBJECTS

В статье я излагаю свои соображения по поводу статьи Евгения Борисова, помещённой в этом выпуске журнала. Попутно я излагаю своё видение проблем кросс-мировой предикации и кросс-идентификации. Я полагаю, что межмировое тождество невозможно и что главная задача состоит в обеспечении идентификации. Для этого можно использовать либо метод поддержания когнитивного контакта либо метод двойников, отождествляемых по набору существенных признаков. Он определяется прагматически. Метод жёстких десигнаторов также ведёт к интенсиональной логике, поскольку в языке-объекте должны присутствовать релятивизованные к мирам имена объектов. Борисов пытается построить логику кросс-мировой предикации сразу на нескольких основаниях, которые плохо совместимы друг с другом. Он квантифицирует по возможным индивидам, но при этом пытается опереться на метаязыковые имена индивидов как на основание для кросс-идентификации, метаязыковое имя индивида становится аргументом для функции значения, хотя не является жёстким десигнатором. Ключевая операция системы Борисова - назначение двойника в возможном мире - спрятана за функцией f, которая выступает в роли условия идентификации, т. е. прочерчивает кросс-мировую линию. На мой взгляд, система имеет потенциал, но нуждается в додумывании и уточнении. In this article, I present my comments on the article by Evgeny Borisov, which is included in this issue of the journal. Along the way, I set out my vision of the problems of cross-world predication and cross-identification. I believe that cross-world identity is impossible, and that the main task is to provide identification. To do this, you can use either the method of keeping cognitive contact, or the method of counterparts identified by a set of essential features, which is defined pragmatically. The method of rigid designators leads to intensional logic, since the object language must contain object names that are relativized to worlds. Borisov is trying to build the logic of cross-world predication on several bases at once, which are poorly compatible with each other. He quantifies over the domain of possible individuals, but at the same time he tries to rely on the metalinguistic names of individuals as a basis for cross-identification, the metalinguistic name of an individual becomes an argument for the value function, although it is not a rigid designator. The key operation of Borisov’s system is the appointment of a counterpart in a possible world. It is hidden behind the function f, which acts as a condition for identification, that is, it draws a cross-world line. In my opinion, the system has some good prospects, but it needs to be thought out and refined.

Search for Appropriate Textual Information Sources

In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.

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.