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基于BERTCA的新闻实体与正文语义相关度计算模型(Semantic Relevance Computing Model of News Entity and Text based on BERTCA)
目前的搜索引擎仍然存在“重形式, 轻语义”的问题, 无法做到对搜索关键词和文本的深层次语义理解, 因此语义检索成为当代搜索引擎中亟需解决的问题。为了提高搜索引擎的语义理解能力, 提出一种语义相关度的计算方法。首先标注金融类新闻标题实体与新闻正文语义相关度语料1万条, 然后建立新闻实体与正文语义相关度计算的BERTCA(Bidirectional Encoder Representation from Transformers Co-Attention)模型, 通过使用BERT预训练模型, 综合考虑细粒度的实体和粗粒度的正文的语义信息, 然后经过协同注意力, 实现实体与正文的语义匹配, 不仅能计算出金融新闻实体与新闻正文之间的相关度, 还能根据相关度阈值来判定相关度类别, 实验表明该模型在1万条标注语料上准确率超过95%, 优于目前主流模型, 最后通过具体搜索示例展现该模型的优秀性能。
Experimentally realized memristive memory augmented neural network
Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size.
Target-based Sentiment Annotation in Chinese Financial News
Based on high quality annotation guideline and effective quality control strategy, a corpus with 8, 314 target-level sentiment annotation is constructed on 6, 336 paragraphs from Chinese financial news text.
The Design and Construction of a Chinese Sarcasm Dataset
Thus, the detection and processing of sarcasm is important to social media analysis. However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset.
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.
Ranked #2 on
Emotion Cause Extraction
on ECE
