Abstract
Dictionaries link a given word to a set of alternative words (the definition) which in turn point to further descendants. Iterating through definitions in this way, one typically finds that definitions loop back upon themselves. We demonstrate that such definitional loops are created in order to introduce new concepts into a language. In contrast to the expectations for a random lexical network, in graphs of the dictionary, meaningful loops are quite short, although they are often linked to form larger, strongly connected components. These components are found to represent distinct semantic ideas. This observation can be quantified by a singular value decomposition, which uncovers a set of conceptual relationships arising in the global structure of the dictionary. Finally, we use etymological data to show that elements of loops tend to be added to the English lexicon simultaneously and incorporate our results into a simple model for language evolution that falls within the ‘‘rich-get-richer’’ class of network growth.
Highlights
Words are the building blocks of language
Iterating through definitions in this way, one typically finds that definitions loop back upon themselves. We demonstrate that such definitional loops are created in order to introduce new concepts into a language
In contrast to the expectations for a random lexical network, in graphs of the dictionary, meaningful loops are quite short, they are often linked to form larger, strongly connected components. These components are found to represent distinct semantic ideas. This observation can be quantified by a singular value decomposition, which uncovers a set of conceptual relationships arising in the global structure of the dictionary
Summary
Words are the building blocks of language. By stringing together chains of these simple lexical units, people can convey complex thoughts and ideas. It is convenient to represent the lexicon as a network With this approach, words are considered to be the nodes of a graph with edges drawn based on a variety of possible relationships such as word co-occurrence in texts, thesauri, or word-association experiments on human users. In previous work [13,14], we described the curvature induced in a network by the clustering of triangles and showed the importance of two-loops in identifying nodes that contribute highly to this curvature Using this approach, we were able to analyze local structures in both the World Wide Web and Email networks [13,14]. Using etymological data, we demonstrate that words within the same loop tend to have been introduced into the English language at similar times, and we incorporate our results into a simple model for language evolution that falls within the ‘‘rich-get-richer’’ class of network growth
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
