Statute Recommendation Based on Word Embedding

The statute recommendation problem is a sub problem of the automated decision system, which can help the legal staff to deal with the process of the case in an intelligent and automated way. In this paper, an improved common word similarity algorithm is proposed for normalization. Meanwhile, word mover’s distance (WMD) algorithm was applied to the similarity measurement and statute recommendation problem, and the problem scene which was originally used for classification was extended. Finally, a variety of recommendation strategies different from traditional collaborative filtering methods were proposed. The experimental results show that it achieves the best value of Fmeasure reaching 0.799. And the comparative experiment shows that WMD algorithm can achieve better results than TF-IDF and LDA algorithm.

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

Behavior Targeting Based on Hierarchical Taxonomy Aggregation for Heterogeneous Online Shopping Applications

Behavior targeting(BT)based on individual web-browsing history has become more valuable in precision marketing for many companies through capturing users’interest and preference.It is common in practice that the behavior data collected from different online shopping applications are inconsistent since they are labelled by different item taxonomy,where the same behavior could have different representations and therefore analysis confusion arises.To address this issue,we propose a semantic similarity based strategy to transform the heterogeneous behavior extracted from deep packet inspection(DPI)data of a telecommunication operator into a unique standard one.The Word Mover’s Distance algorithm is exploited to evaluate the semantic similarity of the distributed representations of two web-browsing histories.Moreover,the architecture of the behavior targeting platform on Hadoop is implemented,which is capable of processing data with size of PB level every day.

基于局部字袋模型的三维部分形状检索方法

提出一种新的基于"局部字袋混合模型"表示的三维部分形状检索方法。该方法首先定义一种支撑范围相互重叠的三维局部字袋模型表示,并在该形状表示下,给出"推土机距离"的基础距离度量的定义,并运用"层次匹配核"方法计算三维模型间部分相似性的具体步骤,显著提高了原方法的效率和鲁棒性。在理论上证明了所提出的距离度量改进了整体字袋模型低估三维形状之间距离的问题。实验结果显示了该方法对三维部分形状检索的有效性。

结合语义和结构的短文本相似度计算

短文本相似度不仅包括语义相似度,还包括语法相似度。目前在短文本相似度度量算法中,大多只分析短文本语义层次的相似性,往往忽略了短文本的语法结构对短文本相似度的重要影响,导致无法捕获大量的文本语义信息,同时在短文本分类任务中召回率不够理想。通过分析短文本的特征,将EMD(earth mover’s distance)求解线性规划中运输问题的最优解应用于度量两个短文本的相似度,用Word2Vec度量两个单词的语义相似性,提出了词序位置相似度的概念,即在计算短文本相似度的同时考虑语句词组顺序对相似度的贡献。实验结果表明,在捕获大量文本语义信息的基础上,将算法应用于k近邻(k-nearest neighbor,KNN)文本分类中,有较好的准确率和召回率。

基于知识块摘要和词转移距离的高效司法文档分类

随着全国司法机关智能化建设的深入推进,通过信息化建设应用所积累的海量司法文书为开展司法智能服务提供了司法数据分析基础。通过司法文书的相似性分析实现类案推送,可以为司法人员提供智能辅助办案决策支持,从而提高办案的质量和效率。针对面向通用领域的文本分类方法因没有考虑特定司法领域文本的复杂结构和知识语义而导致司法文本分类的效能低问题,提出一种基于司法知识块摘要和词转移距离(WMD)的高效司法文档分类方法。首先为司法文书构建领域本体知识模型,进而基于领域本体,利用信息抽取技术获取司法文档中核心知识块摘要;然后基于司法文本的知识块摘要利用WMD进行司法文档相似度计算;最后利用K最近邻算法进行司法文本分类。以两个典型罪名的案件文档集作为实验数据,与传统的WMD文档相似度计算方法进行对比,实验结果表明,所提方法能明显提高司法文本分类的正确率(分别有5.5和9.9个百分点的提升),同时也降低了文档分类所需的时间(速度分别提升到原来的52.4和89.1倍)。

一种基于依存句法和WRD的句子相似计算方法

句子相似计算技术能够得到两个句子的相似关系,在敏感数据检测领域具有广阔的应用前景。因此,提出了一种融合依存句法和Word Rotator’s Distance语义距离的句子相似计算方法。通过依存句法分析句子结构并提取各种依存关系词集,计算对应依存关系词集间的Word Rotator’s Distance语义距离,并最终得到两个句子的相似指数。实验证明,该方法能够得到比较准确的句子相似计算结果,适用于多种敏感数据检测场景。