TY - JOUR
T1 - Sidestepping the Combinatorial Explosion
T2 - An Explanation of n-gram Frequency Effects Based on Naive Discriminative Learning
AU - Baayen, R. Harald
AU - Hendrix, Peter
AU - Ramscar, Michael
PY - 2013/9
Y1 - 2013/9
N2 - Arnon and Snider ((2010). More than words: Frequency effects for multi-word phrases. Journal of Memory and Language, 62, 67-82) documented frequency effects for compositional four-grams independently of the frequencies of lower-order n-grams. They argue that comprehenders apparently store frequency information about multi-word units. We show that n-gram frequency effects can emerge in a parameter-free computational model driven by naive discriminative learning, trained on a sample of 300,000 four-word phrases from the British National Corpus. The discriminative learning model is a full decomposition model, associating orthographic input features straightforwardly with meanings. The model does not make use of separate representations for derived or inflected words, nor for compounds, nor for phrases. Nevertheless, frequency effects are correctly predicted for all these linguistic units. Naive discriminative learning provides the simplest and most economical explanation for frequency effects in language processing, obviating the need to posit counters in the head for, and the existence of, hundreds of millions of n-gram representations.
AB - Arnon and Snider ((2010). More than words: Frequency effects for multi-word phrases. Journal of Memory and Language, 62, 67-82) documented frequency effects for compositional four-grams independently of the frequencies of lower-order n-grams. They argue that comprehenders apparently store frequency information about multi-word units. We show that n-gram frequency effects can emerge in a parameter-free computational model driven by naive discriminative learning, trained on a sample of 300,000 four-word phrases from the British National Corpus. The discriminative learning model is a full decomposition model, associating orthographic input features straightforwardly with meanings. The model does not make use of separate representations for derived or inflected words, nor for compounds, nor for phrases. Nevertheless, frequency effects are correctly predicted for all these linguistic units. Naive discriminative learning provides the simplest and most economical explanation for frequency effects in language processing, obviating the need to posit counters in the head for, and the existence of, hundreds of millions of n-gram representations.
KW - computational modeling
KW - n-gram frequency effects
KW - Naive discriminative learning
KW - Rescorla-Wagner equations
UR - http://www.scopus.com/inward/record.url?scp=84884147733&partnerID=8YFLogxK
U2 - 10.1177/0023830913484896
DO - 10.1177/0023830913484896
M3 - Article
C2 - 24416960
AN - SCOPUS:84884147733
SN - 0023-8309
VL - 56
SP - 329
EP - 347
JO - Language and Speech
JF - Language and Speech
IS - 3
ER -