It is commonly assumed across the language sciences that some semantic participant information is lexically encoded in the representation of verbs and some is not. In this paper, we propose that semantic obligatoriness and verb class specificity are criteria which influence whether semantic information is lexically encoded. We present a comprehensive survey of the English verbal lexicon, a sentence continuation study, and an on-line sentence processing study which confirm that both factors play a role in the lexical encoding of participant information.
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http://dx.doi.org/10.1016/s0010-0277(03)00082-9 | DOI Listing |
J Autism Dev Disord
January 2025
The First Hospital of Jinan University, Guangzhou, China.
Purpose: Children with autism spectrum disorder (ASD) often show abnormal speech prosody. Tonal languages can pose more difficulties as speakers need to use acoustic cues to make lexical contrasts while encoding the focal function, but the acquisition of speech prosody of non-native languages, especially tonal languages has rarely been investigated.
Methods: This study aims to fill in the aforementioned gap by studying prosodic focus-marking in Mandarin by native Cantonese-speaking children with ASD (n = 25), in comparison with their typically developing (TD) peers (n = 20) and native Mandarin-speaking children (n = 20).
. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words.
View Article and Find Full Text PDFTop Cogn Sci
December 2024
Department of Linguistics, University of Massachusetts Amherst.
As they process complex linguistic input, language comprehenders must maintain a mapping between lexical items (e.g., morphemes) and their syntactic position in the sentence.
View Article and Find Full Text PDFEncoding and establishing a new second-language (L2) phonological category is notoriously difficult. This is particularly true for phonological contrasts that do not exist in the learners' native language (L1). Phonological categories that also exist in the L1 do not seem to pose any problems.
View Article and Find Full Text PDFPeerJ Comput Sci
October 2024
Department of Basic, Xi'an Research Institute of High-Tech, Xi'an, Shaanxi, China.
Lexicon Enhanced Bidirectional Encoder Representations from Transformers (LEBERT) has achieved great success in Chinese Named Entity Recognition (NER). LEBERT performs lexical enhancement with a Lexicon Adapter layer, which facilitates deep lexicon knowledge fusion at the lower layers of BERT. However, this method is likely to introduce noise words and does not consider the possible conflicts between words when fusing lexicon information.
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