Sentence examples for morphological system from inspiring English sources

A large-scale channel-shoal morphological system has been historically present in the outer bay.

This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE).

The exact constraints of such a morphological system, the rules of insertion for the derivational morphemes, are still difficult to determine.

The Arabic morphological system applies different generation rules on a given root to generate a list of words from the same root.

This research proposes the possibility for testing variants of a morphological system using physical models as the precedent while incorporating multiple means of computational analysis for extensive exploration.

Rich morphological system makes this architecture inefficient if it is used in rich morphological language applications since it needs high computational costs to train and use these models within real-time applications, such as speech recognition and automatic machine translation.

All purely morphological systems (i.e., classification schemes based on structural features) are artificial.

The vocabulary size varies from languages such as English that have simple morphological systems to languages that have richer morphological systems such as the Arabic language [7] (where every single root can be converted to several other valid forms using morphological derivation rules).

The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem.

The results of this study show that the SDP TF approach can be used successfully to develop statistically robust models for describing nonlinearity in beach morphological systems.

In this work, we try to build recurrent neural network-based language models that can handle the network training speed problem with languages that have rich morphological systems based on word tokenization.