The Morphology of Indonesian

Denistia, Karlina; Baayen, R. Harald

doi:10.4324/9781003090205-41

Cited by 6 publications

(7 citation statements)

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“…This is usually done when the verb is transitive and requires an object. For example, in sentence (12), the noun /surat/ 'letter' functions as the object-intensifier of the verb /menyurati/ 'to write a letter' (-occurrence: 3,427). Using the /me(N)-/ prefix helps clarify the relationship between the verb and its object in the sentence.…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, recent study has adopted corpus-based and computational methods to research morphological processes in Indonesian (e.g., Amalia et al, 2021). Notable works by Denistia and Baayen (2022) utilize quantitative approaches to investigate the frequency, distribution, and usage trends of morphemes, providing empirical evidence to support theoretical frameworks. Nevertheless, the prefix /me(N)-/ has not been thoroughly covered in these researches.…”

Section: B Previous Researchmentioning

confidence: 99%

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Analyzing Prefix /me(N)-/ in the Indonesian Affixation: A Corpus-Based Morphology

Nugraha

2024

tpls

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This paper presents a corpus-based study of the prefix /me(N)-/ in Indonesian, a productive affix used to form predicative constructions. The analysis was based on the Indonesian – Leipzig Corpora Collection (ILCC). It explored the prefix’s distribution, functions, and productivity, focusing on its role in encoding semantic and syntactic features. The results showed that the prefix /me(N)-/ was predominantly used to derive verbs, accounting for over 68,7% at the mean value of 7,1. However, /me(N)-/ was also employed to derive adjectives, albeit less frequently (19.4%), at the mean value of 6,3, and about 12% of forming new words from noun stems (mean value was about 5,9). Semantically, the prefix can uniquely express causative (49,24% at a mean value of 43), instrumental (11,45% at a mean value of 10), reflexive (19,47 at a mean value of 17), possessive/state meanings (10,31% at a mean value of 9), and reciprocal (9,54% at a mean value of 8,3). Derived verbs with the prefix /me(N)-/ had syntactic functions influenced by their semantic meaning. Some of the most common and distinctive syntactic functions of /me(N)-/ are form monovalent (29,18% having a mean value of 27,33), bivalent (48,40% having a mean value of 45,33), and trivalent (22,42% having a mean value of 21). The study demonstrated that /me(N)-/ was highly productive. Future research could examine the cognitive and processing mechanisms involved in understanding /me(N)-/-derived words.

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Section: Discussionmentioning

confidence: 99%

Section: B Previous Researchmentioning

confidence: 99%

Analyzing Prefix /me(N)-/ in the Indonesian Affixation: A Corpus-Based Morphology

Nugraha

2024

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“…Such a change could add to the difficulty of identifying the prefix and the root in a word. Denistia and Baayen (2021) presented another hypothesis as to why some prefixes may facilitate word recognition. They provided evidence from Indonesian that prefixes can differ in how reliable they are as cues to word meaning.…”

Section: Discussionmentioning

confidence: 99%

“…Of these studies, most are conducted in Finnish (e.g., Bertram & Hyönä, 2003;Bertram et al, 1999;Bertram et al, 2000;Järvikivi et al, 2006;Moscoso del Prado Martín et al, 2004;Niemi et al, 1994;Vannest et al, 2002), but there have also been some studies of morphological processing in other agglutinative languages, such as Korean (e.g., Bae et al, 2021;Kim et al, 2015;Kwon et al, 2012;Pae et al, 2020), Japanese (e.g., Clahsen & Ikemoto, 2012;Fiorentino et al, 2016;Nakano et al, 2016), Estonian (e.g., Lõo, et al, 2018a;Lõo, et al, 2018b), and Turkish (e.g., Fowler et al, 2003;Gürel, 1999;Kirkici & Clahsen, 2013). In addition, Denistia and Baayen (2021) developed a computational model of morphological processing in Indonesian, which is closely related to Malay. However, we are unaware of a study of morphological processing in Malay in the published literature, and therefore a secondary aim of the current project was to begin to explore morphological processing in that language.…”

Section: Agglutinative Languagesmentioning

confidence: 99%

Malay Lexicon Project 2: Morphology in Malay word recognition

et al. 2022

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Morphological processing in visual word recognition has been extensively studied in a few languages, but other languages with interesting morphological systems have received little attention. Here, we examined Malay, an Austronesian language that is agglutinative. Agglutinative languages typically have a large number of morphemes per word. Our primary aim was to facilitate research on morphological processing in Malay by augmenting the Malay Lexicon Project (a database containing lexical information for almost 10,000 words) to include a breakdown of the words into morphemes as well as morphological properties for those morphemes. A secondary goal was to determine which morphological variables influence Malay word recognition. We collected lexical decision data for Malay words that had one prefix and one suffix, and first examined the predictive power of 15 morphological and four lexical variables on response times (RT). Of these variables, two lexical and three morphological variables emerged as strong predictors of RT. In GAMM models, we found a facilitatory effect of root family size, and inhibitory effects of prefix length and prefix percentage of more frequent words (PFMF) on RT. Next, we explored the interactions between overall word frequency and several of these predictors. Of particular interest, there was a significant word frequency by root family size interaction in which the effect of root family size is stronger for lowfrequency words. We hope that this initial work on morphological processing in Malay inspires further research in this and other understudied languages, with the goal of developing a universal theory of morphological processing.

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“…The initial stage of speech production is modeled as involving a mapping in the opposite direction, starting with a high-dimensional semantic vector (known as embeddings in computational linguistics) and targeting a vector specifying which phone combinations drive articulation. The DLM has been successful in modeling a range of different morphological systems (e.g., Chuang et al, 2020 , 2022 ; Denistia and Baayen, 2021 ; Heitmeier et al, 2021 ; Nieder et al, 2023 ) as well as behavioral data such as acoustic durations (Schmitz et al, 2021 ; Stein and Plag, 2021 ; Chuang et al, 2022 ), (primed) lexical decision reaction times (Gahl and Baayen, 2023 ; Heitmeier et al, 2023b ), and data from patients with aphasia (Heitmeier and Baayen, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Frequency effects in linear discriminative learning

Heitmeier,

Chuang,

Axen

et al. 2024

Front. Hum. Neurosci.

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Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM) models lexical processing with mappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that the mappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.

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The Morphology of Indonesian

Cited by 6 publications

References 0 publications

Analyzing Prefix /me(N)-/ in the Indonesian Affixation: A Corpus-Based Morphology

Analyzing Prefix /me(N)-/ in the Indonesian Affixation: A Corpus-Based Morphology

Malay Lexicon Project 2: Morphology in Malay word recognition

Frequency effects in linear discriminative learning

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