  The wide use of black-box models in natural language processing brings great
challenges to the understanding of the decision basis, the trustworthiness of
the prediction results, and the improvement of the model performance. The words
in text samples have properties that reflect their semantics and contextual
information, such as the part of speech, the position, etc. These properties
may have certain relationships with the word saliency, which is of great help
for studying the explainability of the model predictions. In this paper, we
explore the relationships between the word saliency and the word properties.
According to the analysis results, we further establish a mapping model,
Seq2Saliency, from the words in a text sample and their properties to the
saliency values based on the idea of sequence tagging. In addition, we
establish a new dataset called PrSalM, which contains each word in the text
samples, the word properties, and the word saliency values. The experimental
evaluations are conducted to analyze the saliency of words with different
properties. The effectiveness of the Seq2Saliency model is verified.
