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- W4285292283 abstract "Word identification from continuous input is typically viewed as a segmentation task. Experiments with human adults suggest that familiarity with syntactic structures in their native language also influences word identification in artificial languages; however, the relation between syntactic processing and word identification is yet unclear. This work takes one step forward by exploring a radically different approach of word identification, in which segmentation of a continuous input is viewed as a process isomorphic to unsupervised constituency parsing. Besides formalizing the approach, this study reports simulations of human experiments with DIORA (Drozdov et al., 2020), a neural unsupervised constituency parser. Results show that this model can reproduce human behavior in word identification experiments, suggesting that this is a viable approach to study word identification and its relation to syntactic processing." @default.
- W4285292283 created "2022-07-14" @default.
- W4285292283 creator A5055697003 @default.
- W4285292283 date "2022-01-01" @default.
- W4285292283 modified "2023-09-26" @default.
- W4285292283 title "Word Segmentation as Unsupervised Constituency Parsing" @default.
- W4285292283 doi "https://doi.org/10.18653/v1/2022.acl-long.283" @default.
- W4285292283 hasPublicationYear "2022" @default.
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