Learning Word-vector Quantization

Abstract

We introduced a new classifier named Learning Word-vector Quantization (LWQ) to solve morphological ambiguities in Turkish, which is an agglutinative language. First, a new and morphologically annotated corpus, and then its datasets are prepared with a series of processes. According to datasets, LWQ finds optimal word-vectors positions by moving them in the Euclidean space. LWQ does morphological disambiguation in two steps: First, it defines all solution candidates of an ambiguous word using a morphological analyzer; second, it chooses the best candidate according to its total distances to neighbor words that are not ambiguous. To show LWQ's performance, we have conducted many tests on the corpus by considering the consistency of classification. In the experiments, we achieve 98.4% correct classification ratio to choose correct parse output, which is an excellent level for the literature.

[Retracted] Using an Optimized Learning Vector Quantization‐ (LVQ‐) Based Neural Network in Accounting Fraud Recognition

“…However, if the indicators are not selected properly, even if the fraud risk identification model is constructed very well, the identification effect is not ideal. is article is based on the fraud triangle theory and selects indicators based on the following three criteria: (1) the selected literature review in this article: in each literature, several indicators have the best results in judging fraud; (2) in view of the difficulty of index data collection and processing, eliminate the indexes that are too complicated and unavailable in the processing process; and (3) important fraud risk identification indicators that have appeared in classic fraud cases: in the literature review part, this article has sorted out the indicators used by a large number of domestic and foreign scholars in the identification of management fraud and screened them in accordance with the above three principles. A total of 11 variables with good discriminating effects were selected and 48 indicators were divided into two pieces of indicators, accounting and nonaccounting indicators.…”

“…For example, the National Antifraud Accounting Reporting Committee (Treadway Committee) defines accounting fraud as “a deliberate or reckless act. Whether it is false reporting or omissions, the result is a major misleading accounting report.” In addition, scholars have also defined accounting fraud [ 1 , 2 ]. Hormozzadefighalati defined accounting fraud as fraudulent use of accounting fraud and other violations or illegal means to seek self-interest, thereby harming the interests of others' intentional behavior [ 3 ].…”