Summary of Lógica y Lingüística: Carlos César Jiménez

00:00:00 - 00:05:00

In the video, Carlos César Jiménez discusses the close relationship between linguistics and logic, noting that traditional approaches to logic have not taken into account syntax very much. He then goes on to discuss various logical systems, describing one that is particularly expressive and useful for modeling complex significances in natural languages. He concludes by discussing the lambda calculus, which is also quite expressive and has recently become fashionable among linguists.

• 00:00:00 Carlos S. Jiménez, a philosopher of science, has a degree in science from the National Autonomous University of Mexico and a previous degree in digital systems from the Instituto Politécnico Nacional. He has taught language for more than 20 years and is particularly known for his work in syntax and semantics. In this talk, he discusses the close relationship between linguistics and logic, noting that traditional approaches to logic have not taken into account syntax very much. He then goes on to discuss various logical systems, describing one that is particularly expressive and useful for modeling complex significances in natural languages. He concludes by discussing the lambda calculus, which is also quite expressive and has recently become fashionable among linguists.
• 00:05:00 In this video, Carlos César Jiménez discusses another type of approach to capturing semantic phenomena and many more. He mentions semántica distribución al es, which is where one relies on models in spaces called vectorial spaces. He states that some words are seen as vectors in these spaces and the similarity in meaning of these words has to do with the distance between vectors. He doesn't think these approaches capture enough structural logic, and what we need currently in linguistics is a fusion of these approaches. These vectorial spaces of high dimensions and strong ligability can be linked with logical calculations, like lambda calculus, to give a lot of expressiveness. This qualitative leap in our technologies will allow us to better understand language, understand it better and maybe even create software that can model it better. This is still in its early stages and there is a lot of work to be done in order to achieve this fusion of approaches.