文本分类作为自然语言领域中的重要任务之一,广泛应用于问答系统、推荐系统以及情感分析等相关任务中。为了提取文本数据中的复杂语义特征信息并捕获全局的图信息,提出一种融合图嵌入和BERT(bidirectional encoder representation from ...文本分类作为自然语言领域中的重要任务之一,广泛应用于问答系统、推荐系统以及情感分析等相关任务中。为了提取文本数据中的复杂语义特征信息并捕获全局的图信息,提出一种融合图嵌入和BERT(bidirectional encoder representation from Transformers)嵌入的文本分类模型。该模型引入双级注意力机制考虑不同类型节点的重要性以及同一类型不同相邻节点的重要性,同时采用BERT预训练模型获得包含上下文信息的嵌入并解决一词多义的问题。该模型把所有单词和文本均视为节点,为整个语料库构建一张异构图,将文本分类问题转化为节点分类问题。将双级注意力机制与图卷积神经网络进行融合,双级注意力机制包含类型级注意力和节点级注意力。类型级注意力机制捕获不同类型的节点对某一节点的重要性,节点级注意力机制可以捕获相同类型的相邻节点对某一节点的重要性。将BERT模型获得的文本中局部语义信息与经图卷积神经网络得到的具有全局信息的图嵌入表示相结合,得到最后的文本嵌入表示,并完成文本分类。在4个广泛使用的公开数据集上与7个基线模型进行对比实验,结果表明本文模型提高了文本分类的准确性。展开更多
The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
在求职招聘市场中,信息不对称导致“逆向选择”,加大了企业招聘和求职者求职的难度。线上招聘平台在疫情时期更加重要,对人岗匹配精度要求更高。传统匹配方式受限,深度学习技术特别是BERT模型和集成模型受到关注。当前学者在研究人岗匹...在求职招聘市场中,信息不对称导致“逆向选择”,加大了企业招聘和求职者求职的难度。线上招聘平台在疫情时期更加重要,对人岗匹配精度要求更高。传统匹配方式受限,深度学习技术特别是BERT模型和集成模型受到关注。当前学者在研究人岗匹配问题时,采用常见的TF-IDF词向量表示方法和Word2Vec词向量表示方法来对中文文本进行表征,但是由于科学的进步,当下用BERT模型能更好地读取文本语义,因此本文将BERT模型引入到人岗匹配领域中,采取了基于BERT模型的词向量表示和LightGBM模型的人岗匹配方法,以提升匹配精确度和效率,与多种机器学习模型的预测结果相比较之后,最终发现,在这两种方法的结合下,在本文所构建的人才是否投递模型中的精确度达到了0.886,在岗位是否认可模型中的精确度达到了0.926,由这两个模型的效果可以看出BERT模型和LightGBM模型的结合,可以为招聘平台提供精准模型。In the job recruitment market, information asymmetry leads to “adverse selection”, which increases the difficulty for both enterprises in hiring and job seekers in finding employment. Online recruitment platforms have become even more crucial during the pandemic, placing higher demands on the accuracy of person-job matching. Traditional matching methods are limited, and deep learning technologies, especially the BERT model and ensemble models, have garnered attention. In current research on person-job fit, scholars often represent Chinese text data using common methods such as TF-IDF word vectors and Word2Vec word vectors. However, due to advancements in science and technology, the BERT model is now better at capturing textual semantics. Therefore, this paper introduces the BERT model into the field of person-job fit. This paper proposes a person-job matching method based on the BERT and ensemble models to improve matching accuracy and efficiency. After comparing the prediction results with various machine learning models, it was ultimately found that with the combination of these two methods, the accuracy of the talent submission model constructed in this paper reached 0.886, and the accuracy of the job acceptance model reached 0.926. The effectiveness of these two models demonstrates that the combination of the BERT model and the LightGBM model can provide a precise model for recruitment platforms.展开更多
文摘文本分类作为自然语言领域中的重要任务之一,广泛应用于问答系统、推荐系统以及情感分析等相关任务中。为了提取文本数据中的复杂语义特征信息并捕获全局的图信息,提出一种融合图嵌入和BERT(bidirectional encoder representation from Transformers)嵌入的文本分类模型。该模型引入双级注意力机制考虑不同类型节点的重要性以及同一类型不同相邻节点的重要性,同时采用BERT预训练模型获得包含上下文信息的嵌入并解决一词多义的问题。该模型把所有单词和文本均视为节点,为整个语料库构建一张异构图,将文本分类问题转化为节点分类问题。将双级注意力机制与图卷积神经网络进行融合,双级注意力机制包含类型级注意力和节点级注意力。类型级注意力机制捕获不同类型的节点对某一节点的重要性,节点级注意力机制可以捕获相同类型的相邻节点对某一节点的重要性。将BERT模型获得的文本中局部语义信息与经图卷积神经网络得到的具有全局信息的图嵌入表示相结合,得到最后的文本嵌入表示,并完成文本分类。在4个广泛使用的公开数据集上与7个基线模型进行对比实验,结果表明本文模型提高了文本分类的准确性。
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
文摘在求职招聘市场中,信息不对称导致“逆向选择”,加大了企业招聘和求职者求职的难度。线上招聘平台在疫情时期更加重要,对人岗匹配精度要求更高。传统匹配方式受限,深度学习技术特别是BERT模型和集成模型受到关注。当前学者在研究人岗匹配问题时,采用常见的TF-IDF词向量表示方法和Word2Vec词向量表示方法来对中文文本进行表征,但是由于科学的进步,当下用BERT模型能更好地读取文本语义,因此本文将BERT模型引入到人岗匹配领域中,采取了基于BERT模型的词向量表示和LightGBM模型的人岗匹配方法,以提升匹配精确度和效率,与多种机器学习模型的预测结果相比较之后,最终发现,在这两种方法的结合下,在本文所构建的人才是否投递模型中的精确度达到了0.886,在岗位是否认可模型中的精确度达到了0.926,由这两个模型的效果可以看出BERT模型和LightGBM模型的结合,可以为招聘平台提供精准模型。In the job recruitment market, information asymmetry leads to “adverse selection”, which increases the difficulty for both enterprises in hiring and job seekers in finding employment. Online recruitment platforms have become even more crucial during the pandemic, placing higher demands on the accuracy of person-job matching. Traditional matching methods are limited, and deep learning technologies, especially the BERT model and ensemble models, have garnered attention. In current research on person-job fit, scholars often represent Chinese text data using common methods such as TF-IDF word vectors and Word2Vec word vectors. However, due to advancements in science and technology, the BERT model is now better at capturing textual semantics. Therefore, this paper introduces the BERT model into the field of person-job fit. This paper proposes a person-job matching method based on the BERT and ensemble models to improve matching accuracy and efficiency. After comparing the prediction results with various machine learning models, it was ultimately found that with the combination of these two methods, the accuracy of the talent submission model constructed in this paper reached 0.886, and the accuracy of the job acceptance model reached 0.926. The effectiveness of these two models demonstrates that the combination of the BERT model and the LightGBM model can provide a precise model for recruitment platforms.
