5 papers · 1 filter
Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
Yuqing Zhang, Tessa Verhoef, Gertjan van Noord +1
Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length min…
Enriching Historical Records: An OCR and AI-Driven Approach for Database Integration
Zahra Abedi, Richard M. K. van Dijk, Gijs Wijnholds +1
This research digitizes and analyzes the Leidse hoogleraren en lectoren 1575-1815 books written between 1983 and 1985, which contain biographic data about professors and curators o…
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu Ürker, Tessa Verhoef +2
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeli…
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM…
NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication
Yuchen Lian, Tessa Verhoef, Arianna Bisazza
Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols.…