Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SHAP values for Explaining CNN-based Text Classification Models   Deep neural networks are increasingly used in natural language processing
(NLP) models. However, the need to interpret and explain the results from
complex algorithms are limiting their widespread adoption in regulated
industries such as banking. There has been recent work on interpretability of
machine learning algorithms with structured data. But there are only limited
techniques for NLP applications where the problem is more challenging due to
the size of the vocabulary, high-dimensional nature, and the need to consider
textual coherence and language structure. This paper develops a methodology to
compute SHAP values for local explainability of CNN-based text classification
models. The approach is also extended to compute global scores to assess the
importance of features. The results are illustrated on sentiment analysis of
Amazon Electronic Review data.

3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int