Comprehensive Implementation of TextCNN for Enhanced Collaboration
  between Natural Language Processing and System Recommendation
Xiaonan Xu Zheng Xu Zhipeng Ling Zhengyu Jin ShuQian Du
Natural Language Processing (NLP) is an important branch of artificial
intelligence that studies how to enable computers to understand, process, and
generate human language. Text classification is a fundamental task in NLP,
which aims to classify text into different predefined categories. Text
classification is the most basic and classic task in natural language
processing, and most of the tasks in natural language processing can be
regarded as classification tasks. In recent years, deep learning has achieved
great success in many research fields, and today, it has also become a standard
technology in the field of NLP, which is widely integrated into text
classification tasks. Unlike numbers and images, text processing emphasizes
fine-grained processing ability. Traditional text classification methods
generally require preprocessing the input model's text data. Additionally, they
also need to obtain good sample features through manual annotation and then use
classical machine learning algorithms for classification. Therefore, this paper
analyzes the application status of deep learning in the three core tasks of NLP
(including text representation, word order modeling, and knowledge
representation). This content explores the improvement and synergy achieved
through natural language processing in the context of text classification,
while also taking into account the challenges posed by adversarial techniques
in text generation, text classification, and semantic parsing. An empirical
study on text classification tasks demonstrates the effectiveness of
interactive integration training, particularly in conjunction with TextCNN,
highlighting the significance of these advancements in text classification
augmentation and enhancement.