Diachronic study of the evolution languages is importance in natural language processing (NLP). Recent years have witnessed a surge computational approaches for detection and characterization lexical semantic change (LSC) due to availability diachronic corpora advancing word representation techniques. We propose Gaussian embedding (w2g)-based method present comprehensive LSC detection. W2g prob...

grammaticalization provides a framework to study the genesis and development of grammatical forms in language. in this framework, it is argued that new grammatical forms are constantly developed from lexical items and the already formed grammatical items may go out of use. in spite of some counterexamples, this is often seen as a unidirectional and cyclic process. in this article the concepts o...

We present a qualitative analysis of the (potentially erroneous) outputs contextualized embedding-based methods for detecting diachronic semantic change. First, we introduce an ensemble method outperforming previously described approaches. This is used as basis in-depth degrees change predicted English words across 5 decades. Our findings show that can often predict high scores which are not un...

This study aims at tracking the various semantic changes of Arabic loanwords in Turkish (ALTs). The loanword data were collected from a number dictionaries including Sapan’s (2005) dictionary and Webster's Turkish-English Thesaurus Dictionary. Six types change are found work ALTs, most frequent which is radical shift represents more than 64% total followed by processes narrowing (20%) widening ...

Semantic change detection (SCD) extends the multiclass (MCD) task to provide not only locations but also detailed land-cover/land-use (LCLU) categories before and after observation intervals. This fine-grained semantic information is very useful in many applications. Recent studies indicate that SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two te...