  In this article, we propose using deep learning and transformer architectures
combined with classical machine learning algorithms to detect and identify text
anomalies in texts. Deep learning model provides a very crucial context
information about the textual data which all textual context are converted to a
numerical representation. We used multiple machine learning methods such as
Sentence Transformers, Auto Encoders, Logistic Regression and Distance
calculation methods to predict anomalies. The method are tested on the texts
data and we used syntactic data from different source injected into the
original text as anomalies or use them as target. Different methods and
algorithm are explained in the field of outlier detection and the results of
the best technique is presented. These results suggest that our algorithm could
potentially reduce false positive rates compared with other anomaly detection
methods that we are testing.
