Form-Based Representation in the Mental Lexicon: Priming (with) Bound Stem Allomorphs in Finnish

Järvikivi, Juhani; Niemi, Jussi

doi:10.1006/brln.2001.2534

Cited by 22 publications

(25 citation statements)

References 19 publications

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“…Using a connectionist network account (e.g., Rueckl, Mikolinski, Raveh, Miner, & Mars, 1997), we may suppose that when a word is processed, the connections among its features (e.g., allomorphs) strengthen and facilitate its subsequent identification. In the same vein, morphologically related words (when they are similar in both form and meaning) prime target words (Crepaldi, Rastle, Coltheart, & Nickels, 2010;Feldman, O'Connor, & Moscoso del Prado Martín, 2009;Järvikivi & Niemi, 2002b;Kielar, Joanisse, & Hare, 2008;Lehtonen, Monahan, & Poeppel, 2011;Morris & Stockall, 2012).…”

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confidence: 99%

“…On the one hand, bound stem allomorphs can be used in morphological processes like forming new derived words and compounds, or in diachrony, when they develop into independent lexical entries. On the other hand, a series of experimental studies with Finnish stem allomorphs by Järvikivi and Niemi (2002a, 2002b2002c showed that, in Finnish, stem allomorphs have lexical 1296…”

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confidence: 99%

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Behavioural and ERP effects of paradigm complexity on visual word recognition

Nikolaev

Pääkkönen

Niemi

et al. 2014

Language, Cognition and Neuroscience

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confidence: 99%

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confidence: 99%

Behavioural and ERP effects of paradigm complexity on visual word recognition

Nikolaev

Pääkkönen

Niemi

et al. 2014

Language, Cognition and Neuroscience

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“…Moreover, there is some evidence that humans may simply memorize allomorphs without applying morphophonological transformations on what they hear (or read) [Järvikivi and Niemi 2002]. In this case, the morphology-learning models presented in this work are perhaps closer to human language processing than the IP model of morphology.…”

Section: Allomorphymentioning

confidence: 85%

Unsupervised models for morpheme segmentation and morphology learning

Creutz¹,

Lagus²

2007

ACM Trans. Speech Lang. Process.

187

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We present a model family called Morfessor for the unsupervised induction of a simple morphology from raw text data. The model is formulated in a probabilistic maximum a posteriori framework. Morfessor can handle highly inflecting and compounding languages where words can consist of lengthy sequences of morphemes. A lexicon of word segments, called morphs, is induced from the data. The lexicon stores information about both the usage and form of the morphs. Several instances of the model are evaluated quantitatively in a morpheme segmentation task on different sized sets of Finnish as well as English data. Morfessor is shown to perform very well compared to a widely known benchmark algorithm, in particular on Finnish data. ACM Reference Format:Creutz, M. and Lagus, K. 2007. Unsupervised models for morpheme segmentation and morphology learning.

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“…In Dutch, the diminutive suffix has two frequent allomorphic variations (-tje and -je), and three less frequent ones (-etje, -pje and -kje) (Daelemans et al 1997). In Finnish, allomorphy appears both in the stem (Järvikivi-Niemi 2002b) and in suffixes (Järvikivi et al 2006). Similarly, in Hungarian, allomorphic variations occur as stem shortening or lengthening (Pléh et al 2002), and as suffixal alternating vowels (Kertész 2003;Hayes-Cziráky Londe 2006).…”

Section: Petar Milin -Emmanuel Keuleers -Dušica Filipović ðUrðevićmentioning

confidence: 99%

Allomorphic responses in Serbian pseudo-nouns as a result of analogical learning

Milin¹,

Keuleers²,

Đurđević³

2011

Acta Linguistica Hungarica

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Allomorphy is a phenomenon that occurs in many languages. Several psycholinguistic studies have shown that allomorphy, if present, co-determines cognitive processing. In the present paper we discuss allomorphic variations of Serbian instrumental singular form of pseudo-nouns as emerging from analogical learning. We compare the predictions derived from memory-based language processing models with results from a previous experimental study with adult Serbian native speakers. Results confirm that the production of suffix allomorphs in Serbian instrumental singular masculine nouns can be accounted for by memory-based learning and simple analogical inferences. The present findings are in line with a growing body of research showing that memory-based learning models make relevant predictions about the cognitive processes involved in various linguistic phenomena.

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Form-Based Representation in the Mental Lexicon: Priming (with) Bound Stem Allomorphs in Finnish

Cited by 22 publications

References 19 publications

Behavioural and ERP effects of paradigm complexity on visual word recognition

Behavioural and ERP effects of paradigm complexity on visual word recognition

Unsupervised models for morpheme segmentation and morphology learning

Allomorphic responses in Serbian pseudo-nouns as a result of analogical learning

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