Large-Scale Modeling of Wordform Learning and Representation Export

@article{citeulike:2872697, abstract = {The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the <i>sequence encoder</i> is used to learn nearly 75,000 wordform representations through exposure to strings of stress-marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot-based codes. Then, two large-scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well-formed pseudowords as opposed to ill-formed (scrambled) pseudowords, and by accounting for variance in well-formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing.}, author = {Sibley, Daragh E. and Kello, Christopher T. and Plaut, David C. and Elman, Jeffrey L.}, citeulike-article-id = {2872697}, citeulike-linkout-0 = {http://dx.doi.org/10.1080/03640210802066964}, doi = {10.1080/03640210802066964}, journal = {Cognitive Science: A Multidisciplinary Journal}, keywords = {autoencoder, connectionism, generalization, models, order\_effects, orthography, phonology, sequence\_learning, srn, word\_learning}, number = {4}, pages = {741--754}, posted-at = {2009-09-08 18:02:39}, priority = {0}, publisher = {Psychology Press}, title = {Large-Scale Modeling of Wordform Learning and Representation}, url = {http://dx.doi.org/10.1080/03640210802066964}, volume = {32}, year = {2008} }

TY - JOUR ID - citeulike:2872697 L3 - citeulike-article-id:2872697 N2 - The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the <i>sequence encoder</i> is used to learn nearly 75,000 wordform representations through exposure to strings of stress-marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot-based codes. Then, two large-scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well-formed pseudowords as opposed to ill-formed (scrambled) pseudowords, and by accounting for variance in well-formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. IS - 4 JF - Cognitive Science: A Multidisciplinary Journal EP - 754 TI - Large-Scale Modeling of Wordform Learning and Representation PB - Psychology Press VL - 32 SP - 741 KW - autoencoder KW - connectionism KW - generalization KW - models KW - order_effects KW - orthography KW - phonology KW - sequence_learning KW - srn KW - word_learning AU - Sibley, Daragh AU - Kello, Christopher AU - Plaut, David AU - Elman, Jeffrey PY - 2008/// UR - http://dx.doi.org/10.1080/03640210802066964 ER -