Research on Text Classification Based on Automatically Extracted Keywords

Abstract

Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.

Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

“…The pre-trained language model (PLM) is driven by a large amount of corpus and can use these data to realize the semantic representation of knowledge contained in a large amount of text to realize downstream tasks. The downstream tasks include natural language processing tasks such as classification (Li et al, 2019b ; Maltoudoglou et al, 2022 ; Ni et al, 2020a , 2020b ), sequence labeling (Dai et al, 2019 ; Li et al, 2020b ), summarization (Chintagunta et al, 2021 ; Lacson et al, 2006 ; Yuan et al, 2021 ), translation (Névéol et al, 2018 ; Nobel et al, 2021 ; Wang et al, 2019 ), generation (Melamud & Shivade, 2019 ; Peng et al, 2019 ; Xiong et al, 2019 ), etc. As one of the new downstream tasks, the translation task, Zhu et al ( 2020 ) previously found that using the pre-trained language model as contextual embedding instead of direct fine-tuning will produce better results.…”

Research on automatic classification of Chinese papers based on LDA model and TF-IDF algorithm

“…In 2020, Ni P et al employed the TextRank algorithm to extract keywords from an original paper while using the genetic algorithm to optimize the model. Accordingly, the keyword topic model can be gradually improved with the input of new data [4].…”

StaResGRU-CNN with CMedLMs: A stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence

“…This is usually done through a large amount of historical data or prior knowledge as the training samples so that the model can learn the information carried in these massive data to solve specific predictive tasks [15,16,17,18,19]. These predictive tasks also play many roles in the field of natural language processing [20,21,22,23,24]. Most predictive NLP tasks can be transformed into discrete data-oriented classification tasks.…”