Bigram Semantic Distance as an Index of Continuous Semantic Flow in Natural Language: Theory, Tools, and Applications

Jamie Reilly, Ann Marie Finley, Celia P. Litovsky, Yoed N. Kenett

Research output: Contribution to journalArticlepeer-review


Much of our understanding of word meaning has been informed through studies of single words. Highdimensional semantic space models have recently proven instrumental in elucidating connections between words. Here we show how bigram semantic distance can yield novel insights into conceptual cohesion and topic flow when computed over continuous language samples. For example, “Cats drink milk” is comprised of an ordered vector of bigrams (cat-drink, drink-milk). Each of these bigrams has a unique semantic distance. These distances in turn may provide a metric of dispersion or the flow of concepts as language unfolds. We offer an R-package (“semdistflow”) that transforms any user-specified language transcript into a vector of ordered bigrams, appending two metrics of semantic distance to each pair. We validated these distance metrics on a continuous stream of simulated verbal fluency data assigning predicted switch markers between alternating semantic clusters (animals, musical instruments, fruit). We then generated bigram distance norms on a large sample of text and demonstrated applications of the technique to a classic work of short fiction, To Build a Fire (London, 1908). In one application, we showed that bigrams spanning sentence boundaries are punctuated by jumps in the semantic distance.We

Original languageEnglish
Pages (from-to)2578-2590
Number of pages13
JournalJournal of Experimental Psychology: General
Issue number9
Early online date20 Apr 2023
StatePublished - Sep 2023


  • language
  • semantic distance
  • semantic memory

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental Neuroscience
  • General Psychology


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