数据析取系统的概念模式及其在客户关系管理中的应用

本文对决策支持系统从管理信息系统析取数据的有关问题进行研究。提出了数据析取系统的四种模式，研究了它们的实现方式，并介绍了在客户关系管理中的应用。     

Using semantic similarity to reduce wrong labels in distant supervision for relation extraction

Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.     

Location extraction from tweets

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.     

微波诱导催化糖精N-烷基化反应

:研究了微波场下用固体碱催化剂干态催化糖精与卤代正庚烷的烷基化反应。探讨了辐射功率、时间、产物提取方式和卤代烷等对产率的影响。结果表明 ,微波场促进了固体碱的催化反应 ,使产率有较大的提高     

基于卷积神经网络的旅游信息关系抽取研究

[目的/意义]在非结构化语料集中抽取知识要素,是实现知识图谱的重要环节,本文探索了应用深度学习中的卷积神经网络（CNN）模型进行旅游领域知识关系抽取方法。[方法/过程]抓取专业旅游网站的相关数据建立语料库,对部分语料进行人工标注作为训练集和测试集,通过Python语言编程实现分词、向量化及CNN模型,进行关系抽取实验。[结果/结论]实验结果表明,应用卷积神经网络对非结构化的旅游文本进行关系抽取时能够取得满意的效果（Precision 0.77,Recall 0.76,F1-measure 0.76）。抽取结果通过人工校对进行优化后,可以为旅游知识图谱构建、领域本体构建等工作奠定基础。     

基于综合特征的新疆地方性肝包虫病图像特征的提取与分析

图像特征提取是图像处理的一个主要环节,是图像处理技术研究和应用的一个重要领域。本文选取了新疆地方性肝包虫病中的单囊型肝包虫和正常肝脏CT图像进行研究,提取了灰度直方图、灰度共生矩阵(GLCM)、kc复杂性三种特征组成综合图像特征。首先对图像进行尺寸归一、去噪、对比度增强的预处理,并对综合特征进行统计学分析,最后应用Fisher判别分析法对特征的分类能力进行评价。实验结果表明运用综合特征对图像分类有较高的准确率,这对基于内容的新疆肝包虫病CT图像的检索的研究奠定了一定的基础。     

正交实验法研究三七皂甙的超声提取

在超声波作用下，用乙醇浸提，从三七中提取三七皂甙．以提取产量和三七皂甙含量为评价指标，利用正交实验L9（3^4）筛选出最佳工艺条件为：超声提取温度45℃，超声时间30min，提取次数2次，乙醇体积分数85％．按优选的最佳工艺实验5次，精制三七皂甙平均收率达到16．3％，为常规浸提法的2倍，Rg，相对标准偏差（RSD）为2．84％，R。相对标准偏差为2．99％．     

萃取剂中微量有机胺的测定

溶剂萃取法在钨的分离、纯化、富集和回收等方面具有比较明显的优点。而萃取剂[φ(N235)=15%,(φ仲辛醇)=20%,(φ航空煤油)=65%]中有机胺的含量直接关系到钨收率的高低。利用甲基橙与有机胺反应生成黄色络合物,进而用氯仿萃取进行比色测定以监测萃取剂中有机胺的含量,从而保证生产的正常运作。     

基于Web的网页信息抽取方法的研究

WWW的迅速发展，使其日益成为人们查找有用数据的重要来源。本文介绍了一种基于Web的信息抽取的实现方法，能够按照规则模式重复地将半结构化网页中的信息自动抽取出来。     

萃取技术和联用技术在铅的形态分析中的应用

铅是环境生物体系和食物中存在的污染元素,其毒性与化学形态密切相关。文章重点概述了国内外近年来萃取技术和联用技术在食品等物质中铅形态分析上的应用进展。