… on the knowledge embedding-based encoding-decoding model, which improved the learning
effect of high-dimensional fusion features. [Results] We examined our new model on the …

… [Results] Compared with the joint ranking model CEDR, our new model was up to 3.06%
higher in the P@20 metric. [Limitations] The data was mainly from engineering consulting …

… Supervised topic models with weighted words: multi-label document classification[J]. Frontiers
… An extension of topic models for text classification: a term weighting approach. Int Conf on …

… NLP applications such as document retrieval, ranking, classification and summarization. …
-of-Word model and topic models including LDA to more complex models such as Denoising …

… [Objective] This paper proposes a new model to improve the traditional text classifiers which
… Then, we utilized the cross-entropy loss function of the traditional text classification to the …

[目的] 根据美国政客在特定时间段内的推特文本数据分析其动态的政治情感极性变化, 辅助情报
分析人员判断美国政治走向和中美关系未来走势.[方法] 提出一种架构, 结合多种深度学习模型, …

… : text embedding based on frequency, text embedding based … -aware LDA model for topic
modeling in a document network[… schemes for document representation based on ranking of …

… blurring in short text classification. This paper proposes a new … Biterm Topic Model(BTM)to
train a fixed number of document-… :on simple word-embedding-based models and associated …

… proposes a classification method for Tibetan news text based on Graph Convolutional
Network (GCN) using the construction relationship between Tibetan syllables and documents. […

[目的] 探索最佳文本聚类数目的优化方法, 为提升文本聚类算法的有效性和质量提供参考.[方法]
结合TF-IDF 和Word2Vec 算法, 提取Top N 关键词向量作为语料库文本特征表达; 结合均值漂移…