Modeling Morphological Processing
13 May 2013-pp 149-172
About: The article was published on 2013-05-13. It has received 183 citations till now.
Citations
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TL;DR: A 2-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed, showing that for pseudo-derived words no special morpho-orthographic segmentation mechanism is required and predicting that productive affixes afford faster response latencies for new words.
Abstract: A 2-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed. The study first presents 2 experiments in Serbian, which reveal for sentential reading the inflectional paradigmatic effects previously observed by Milin, Filipovic Đurđevic, and Moscoso del Prado Martin (2009) for unprimed lexical decision. The empirical results are successfully modeled without having to assume separate representations for inflections or data structures such as inflectional paradigms. In the next step, the same naive discriminative learning approach is pitted against a wide range of effects documented in the morphological processing literature. Frequency effects for complex words as well as for phrases (Arnon & Snider, 2010) emerge in the model without the presence of whole-word or whole-phrase representations. Family size effects (Moscoso del Prado Martin, Bertram, Haikio, Schreuder, & Baayen, 2004; Schreuder & Baayen, 1997) emerge in the simulations across simple words, derived words, and compounds, without derived words or compounds being represented as such. It is shown that for pseudo-derived words no special morpho-orthographic segmentation mechanism, as posited by Rastle, Davis, and New (2004), is required. The model also replicates the finding of Plag and Baayen (2009) that, on average, words with more productive affixes elicit longer response latencies; at the same time, it predicts that productive affixes afford faster response latencies for new words. English phrasal paradigmatic effects modulating isolated word reading are reported and modeled, showing that the paradigmatic effects characterizing Serbian case inflection have crosslinguistic scope.
392 citations
Cites background from "Modeling Morphological Processing"
...The parallel dual route models of Frauenfelder and Schreuder (1992); Schreuder and Baayen (1995); Baayen et al. (1997) allow whole-word and constituent access representations to race for word recognition....
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27 Sep 2012
TL;DR: This chapter discusses modes of interaction between the lexicon and the grammar, and the role of token frequency in the emergence of stem-level cyclicity in Bloomfield's lexicon.
Abstract: 3 Storage vs computation 3.1 Preview: modes of interaction between the lexicon and the grammar 3.2 Bloomfield’s lexicon and SPE’s evaluation measure 3.3 Lexical redundancy rules 3.3.1 Lexical redundancy at the stem level 3.3.2 The emergence of stem-level cyclicity: órigin, oríginal, orìginálity 3.3.3 The role of token frequency: còmp[ə]nsátion and cònd[ɛ ]nsátion 3.4 Distributed associative memory 3.5 Refined dual-route models
121 citations
TL;DR: The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon that embraces the discrim inative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminatives.
Abstract: The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian ‘hidden’ constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.
110 citations
Cites background from "Modeling Morphological Processing"
...…conceptualization is very close to the currently prevalent way of thinking in psycholinguistics, which has adopted a form of naive realism in which word meanings are typically associated with monadic concepts (see, e.g. Roelofs, 1997; Levelt et al., 1999; Taft, 1994; Schreuder and Baayen, 1995)....
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...This conceptualization is very close to the currently prevalent way of thinking in psycholinguistics, which has adopted a form of naive realism in which word meanings are typically associated with monadic concepts (see, e.g. Roelofs, 1997; Levelt et al., 1999; Taft, 1994; Schreuder and Baayen, 1995)....
[...]
TL;DR: For instance, the authors showed that children first acquire elementary decoding skills, and then gradually apply these skills with greater accuracy and speed, leading to an increasingly automated process of that recognizes multiletter units (consonant clusters, syllables, and morphemes) and whole words.
Abstract: Word identification, which is the retrieval of the linguistic constituents (phonological, semantic) of a word, plays a central role in children's reading development. This development includes the automatization of word decoding and the attainment of fluent reading levels, both essential for skilled reading with comprehension (Perfetti, 1992; Stanovich, 2000; Verhoeven & van Leeuwe, 2009). In learning to read, children first acquire elementary decoding skills, and then gradually apply these skills with greater accuracy and speed, leading to an increasingly automated process of that recognizes multiletter units (consonant clusters, syllables, and morphemes) and whole words (Ehri, 2005). Automatic word recognition enables the devotion of mental resources to the meaning of a text and thus allows readers to use reading as a tool for the acquisition of new information and knowledge (Perfetti, 1998; Stanovich, 2000).
96 citations
Cites background from "Modeling Morphological Processing"
...acquisition: A cross-linguistic perspective LUDO VERHOEVEN Radboud University Nijmegen...
[...]
...These two accounts have been combined in more interactive models, which propose a direct lexical route involving access to full-form representations along with a parsing route (cf. Caramazza, Laudanna, & Romani, 1988; Plaut et al., 1996; Schreuder & Baayen, 1995)....
[...]
TL;DR: Key findings in spoken-word recognition by humans are summarized and how models of spoken- word recognition account for them are described.
Abstract: All words of the languages we know are stored in the mental lexicon. Psycholinguistic models describe in which format lexical knowledge is stored and how it is accessed when needed for language use. The present article summarizes key findings in spoken-word recognition by humans and describes how models of spoken-word recognition account for them. Although current models of spoken-word recognition differ considerably in the details of implementation, there is general consensus among them on at least three aspects: multiple word candidates are activated in parallel as a word is being heard, activation of word candidates varies with the degree of match between the speech signal and stored lexical representations, and activated candidate words compete for recognition. No consensus has been reached on other aspects such as the flow of information between different processing levels, and the format of stored prelexical and lexical representations. WIREs Cogn Sci 2012, 3:387-401. doi: 10.1002/wcs.1178 For further resources related to this article, please visit the WIREs website.
96 citations
References
More filters
TL;DR: A 2-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed, showing that for pseudo-derived words no special morpho-orthographic segmentation mechanism is required and predicting that productive affixes afford faster response latencies for new words.
Abstract: A 2-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed. The study first presents 2 experiments in Serbian, which reveal for sentential reading the inflectional paradigmatic effects previously observed by Milin, Filipovic Đurđevic, and Moscoso del Prado Martin (2009) for unprimed lexical decision. The empirical results are successfully modeled without having to assume separate representations for inflections or data structures such as inflectional paradigms. In the next step, the same naive discriminative learning approach is pitted against a wide range of effects documented in the morphological processing literature. Frequency effects for complex words as well as for phrases (Arnon & Snider, 2010) emerge in the model without the presence of whole-word or whole-phrase representations. Family size effects (Moscoso del Prado Martin, Bertram, Haikio, Schreuder, & Baayen, 2004; Schreuder & Baayen, 1997) emerge in the simulations across simple words, derived words, and compounds, without derived words or compounds being represented as such. It is shown that for pseudo-derived words no special morpho-orthographic segmentation mechanism, as posited by Rastle, Davis, and New (2004), is required. The model also replicates the finding of Plag and Baayen (2009) that, on average, words with more productive affixes elicit longer response latencies; at the same time, it predicts that productive affixes afford faster response latencies for new words. English phrasal paradigmatic effects modulating isolated word reading are reported and modeled, showing that the paradigmatic effects characterizing Serbian case inflection have crosslinguistic scope.
392 citations
27 Sep 2012
TL;DR: This chapter discusses modes of interaction between the lexicon and the grammar, and the role of token frequency in the emergence of stem-level cyclicity in Bloomfield's lexicon.
Abstract: 3 Storage vs computation 3.1 Preview: modes of interaction between the lexicon and the grammar 3.2 Bloomfield’s lexicon and SPE’s evaluation measure 3.3 Lexical redundancy rules 3.3.1 Lexical redundancy at the stem level 3.3.2 The emergence of stem-level cyclicity: órigin, oríginal, orìginálity 3.3.3 The role of token frequency: còmp[ə]nsátion and cònd[ɛ ]nsátion 3.4 Distributed associative memory 3.5 Refined dual-route models
121 citations
TL;DR: The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon that embraces the discrim inative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminatives.
Abstract: The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian ‘hidden’ constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.
110 citations
TL;DR: For instance, the authors showed that children first acquire elementary decoding skills, and then gradually apply these skills with greater accuracy and speed, leading to an increasingly automated process of that recognizes multiletter units (consonant clusters, syllables, and morphemes) and whole words.
Abstract: Word identification, which is the retrieval of the linguistic constituents (phonological, semantic) of a word, plays a central role in children's reading development. This development includes the automatization of word decoding and the attainment of fluent reading levels, both essential for skilled reading with comprehension (Perfetti, 1992; Stanovich, 2000; Verhoeven & van Leeuwe, 2009). In learning to read, children first acquire elementary decoding skills, and then gradually apply these skills with greater accuracy and speed, leading to an increasingly automated process of that recognizes multiletter units (consonant clusters, syllables, and morphemes) and whole words (Ehri, 2005). Automatic word recognition enables the devotion of mental resources to the meaning of a text and thus allows readers to use reading as a tool for the acquisition of new information and knowledge (Perfetti, 1998; Stanovich, 2000).
96 citations
TL;DR: Key findings in spoken-word recognition by humans are summarized and how models of spoken- word recognition account for them are described.
Abstract: All words of the languages we know are stored in the mental lexicon. Psycholinguistic models describe in which format lexical knowledge is stored and how it is accessed when needed for language use. The present article summarizes key findings in spoken-word recognition by humans and describes how models of spoken-word recognition account for them. Although current models of spoken-word recognition differ considerably in the details of implementation, there is general consensus among them on at least three aspects: multiple word candidates are activated in parallel as a word is being heard, activation of word candidates varies with the degree of match between the speech signal and stored lexical representations, and activated candidate words compete for recognition. No consensus has been reached on other aspects such as the flow of information between different processing levels, and the format of stored prelexical and lexical representations. WIREs Cogn Sci 2012, 3:387-401. doi: 10.1002/wcs.1178 For further resources related to this article, please visit the WIREs website.
96 citations