Structure of Dictionary Entries of Bangla morphemes for morphological rule generation for Universal Networking Language

Author(s):  
Muhammad Firoz Mridha ◽  
Mohammad Nurul Huda ◽  
Md. Sadequr Rahman ◽  
Chowdhury Mofizur Rahman
2019 ◽  
Vol 62 (10) ◽  
pp. 3790-3807 ◽  
Author(s):  
Sara Ferman ◽  
Liat Kishon-Rabin ◽  
Hila Ganot-Budaga ◽  
Avi Karni

Purpose The purpose of this study was to delineate differences between children with specific language impairment (SLI), typical age–matched (TAM) children, and typical younger (TY) children in learning and mastering an undisclosed artificial morphological rule (AMR) through exposure and usage. Method Twenty-six participants (eight 10-year-old children with SLI, 8 TAM children, and ten 8-year-old TY children) were trained to master an AMR across multiple training sessions. The AMR required a phonological transformation of verbs depending on a semantic distinction: whether the preceding noun was animate or inanimate. All participants practiced the application of the AMR to repeated and new (generalization) items, via judgment and production tasks. Results The children with SLI derived significantly less benefit from practice than their peers in learning most aspects of the AMR, even exhibiting smaller gains compared to the TY group in some aspects. Children with SLI benefited less than TAM and even TY children from training to judge and produce repeated items of the AMR. Nevertheless, despite a significant disadvantage in baseline performance, the rate at which they mastered the task-specific phonological regularities was as robust as that of their peers. On the other hand, like 8-year-olds, only half of the SLI group succeeded in uncovering the nature of the AMR and, consequently, in generalizing it to new items. Conclusions Children with SLI were able to learn language aspects that rely on implicit, procedural learning, but experienced difficulties in learning aspects that relied on the explicit uncovering of the semantic principle of the AMR. The results suggest that some of the difficulties experienced by children with SLI when learning a complex language regularity cannot be accounted for by a broad, language-related, procedural memory disability. Rather, a deficit—perhaps a developmental delay in the ability to recruit and solve language problems and establish explicit knowledge regarding a language task—can better explain their difficulties in language learning.


2009 ◽  
Vol 20 (10) ◽  
pp. 2655-2666 ◽  
Author(s):  
Dong LIU ◽  
Xiang-Wu MENG ◽  
Jun-Liang CHEN ◽  
Ya-Mei XIA

2021 ◽  
Vol 11 (8) ◽  
pp. 3347
Author(s):  
Siqi Ma ◽  
Xin Wang ◽  
Xiaochen Wang ◽  
Hanyu Liu ◽  
Runtong Zhang

Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train’s internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults.


1996 ◽  
Vol 05 (01n02) ◽  
pp. 99-112 ◽  
Author(s):  
NING SHAN ◽  
HOWARD J. HAMILTON ◽  
NICK CERCONE

We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascen sion technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, a reduction technique is applied to generate a minimalized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.


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