PIRSA:19110050

Video URL

https://pirsa.org/19110050

What is the landscape of natural language? Insights from a random language model

De Giuli, E. (2019). What is the landscape of natural language? Insights from a random language model. Perimeter Institute for Theoretical Physics. https://pirsa.org/19110050

De Giuli, Eric. What is the landscape of natural language? Insights from a random language model. Perimeter Institute for Theoretical Physics, Nov. 06, 2019, https://pirsa.org/19110050 

          @misc{ scivideos_PIRSA:19110050,
            doi = {10.48660/19110050},
            url = {https://pirsa.org/19110050},
            author = {De Giuli, Eric},
            keywords = {Other Physics},
            language = {en},
            title = {What is the landscape of natural language? Insights from a random language model},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {nov},
            note = {PIRSA:19110050 see, \url{https://scivideos.org/index.php/pirsa/19110050}}
          }

Eric De Giuli Toronto Metropolitan University

Talk numberPIRSA:19110050
DOI10.48660/19110050
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Many complex systems have a generative, or linguistic, aspect: life is written in the language of DNA; protein structure is written in a language of amino acids, and human endeavour is often written in text. Are there universal aspects of the relationship between sequence and structure? I am trying to answer this question using models of random languages. Recently I proposed a model of random context-free languages [1] and showed using simulations that the model has a transition from an unintelligent phase to an ordered phase. In the former, produced sequences look like noise, while in the latter they have a nontrivial Shannon entropy; thus the transition corresponds to the emergence of information-carrying in the language. 

In this talk I will explain the basics of natural language syntax, without assuming any prior knowledge of linguistics. I will present the results from the model above, and explain how the model is related to complex matrix models with disorder [2].

 

[1] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.128301

[2] https://arxiv.org/abs/1902.07516