Mental Algorithms in the Historical Emergence of Word Meanings

Words frequently acquire new senses, but the mental process that underlies the historical emergence of these senses is often opaque. Many have suggested that word meanings develop in non-arbitrary ways, but no attempt has been made to formalize these proposals and test them against historical data at scale. We propose that word meaning extension should reflect a drive towards cognitive economy. We test this proposal by exploring a family of computational models that predict the evolution of word senses, evaluated against a large digitized lexicon that dates back 1000 years in English language history. Our findings suggest that word meanings not only extend in predictable ways, but also that they do so following an historical path that tends to minimize cognitive cost through a process of nearestneighbor chaining. Our work contributes a formal approach to reverse-engineering mental algorithms of the human lexicon.

[1]  Steven T Piantadosi,et al.  Word lengths are optimized for efficient communication , 2011, Proceedings of the National Academy of Sciences.

[2]  Manaal Faruqui,et al.  Non-distributional Word Vector Representations , 2015, ACL.

[3]  J. Gower,et al.  Minimum Spanning Trees and Single Linkage Cluster Analysis , 1969 .

[4]  Yang Xu,et al.  Historical Semantic Chaining and Efficient Communication: The Case of Container Names , 2016, Cogn. Sci..

[5]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[6]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[7]  G. Lakoff,et al.  Women, Fire, and Dangerous Things: What Categories Reveal about the Mind , 1988 .

[8]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[9]  L. Wittgenstein.  Philosophical investigations = Philosophische Untersuchungen , 1958 .

[10]  Charles Kemp,et al.  Kinship Categories Across Languages Reflect General Communicative Principles , 2012, Science.

[11]  Silvia P. Gennari,et al.  Knowing versus Naming: Similarity and the Linguistic Categorization of Artifacts , 1999 .

[12]  Dolores Torres Medina.  Diachronic prototype semantics: A contribution to historical lexicology , 1998 .

[13]  Daniel Jurafsky,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2009, Prentice Hall series in artificial intelligence.

[14]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[15]  Felix Hill,et al.  SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation , 2014, CL.

[16]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[17]  S. Kirby,et al.  Compression and communication in the cultural evolution of linguistic structure , 2015, Cognition.

[18]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[19]  E. Rosch.  Cognitive Representations of Semantic Categories. , 1975 .

[20]  M. Breal.  Essai de sémantique : (science des significations) , 1924 .

[21]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[22]  R. Prim.  Shortest connection networks and some generalizations , 1957 .

[23]  Ehud Rivlin,et al.  Placing search in context: the concept revisited , 2002, TOIS.

[24]  Mahesh Srinivasan,et al.  Evolution of polysemous word senses from metaphorical mappings , 2016, CogSci.

[25]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[26]  Joan L. Bybee,et al.  From Usage to Grammar: The Mind's Response to Repetition , 2007 .