Feature-enriched matrix factorization for relation extraction

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.     

一种基于卷积神经网络和双向长短记忆神经网络的在线医疗社区问答文本实体识别方法

[目的/意义]针对在线医疗社区问答文本复杂程度高、结构化程度低的特点,结合卷积神经网络（CNN）和双向长短记忆神经网络（BiLSTM）两种深度学习模型以及条件随机场（CRF）模型,提出一套适用于在线医疗问答文本的实体识别方法并进行验证。 [过程/方法] 将问答文本进行清洗和BIO标注后,分别用CNN和BiLSTM进行字级别的特征抽取,将两种模型抽取到的特征进行融合,后放入CRF中训练出实体预测模型,再将问答文本放入训练好的模型中得到最终的实体识别结果。[结果/结论]在所选取的乳腺癌医疗社区问答文本数据集上,所提出的方法结果优于其他模型,且识别准确率达到92.3%,召回率达到89.3%,F值达到90.8%。     

计算机抽站法在青海巴州沟流域雨量站网密度分析中的应用

本文应用计算机随机抽站和组合抽站两方法,对青海湟水支流巴州沟流域配套雨量站网按６、１２、２４小时三种时段,四种误差分别进行计算后,确定了该流域的合理布站密度。     

基于模糊处理的中文文本关键词提取算法*

研究关键词提取算法,在分析可能影响关键词提取词语的各种属性并将其量化的基础上,提出并实现一种将分词与词性标注、文本预处理、线性加权算法、组合词生成与过滤、合并候选关键词等集成到一个完整框架中的模型算法。     

在“语言狂欢”中寻幽探胜——读林伦伦的《新词语漫话》简

林伦伦著《新词语漫话》一书具有敏锐性、学术性、可读性、本土性、实用性诸特点,是一部在网络新词语的“语言狂欢”中寻幽探胜之作。     

涪陵胭脂萝卜红色素的提取与分析

以涪陵胭脂萝卜为原料，提取天然食用萝卜红色素．采用大孔吸附树脂富集的新工艺，提高此天然色素的提取效率和纯度，并通过对实验中提取的萝卜红色素性能的测试和分析，表明该工艺所提取的色素是优质的天然色素．     

不同工艺对茶多酚提取效率和质量的影响研究

本文比较分析了目前从茶叶中提取茶多酚的四种工艺的主要特点和不足,并比较了工艺条件对茶多酚的提取效率和质量的影响.结果表明,提取率较好,茶多酚含量较高是金属离子盐沉淀和树脂吸附茶多酚提取工艺.     

军备情报说明文的武器对象判定

军备情报说明文往往都是围绕一个或几个武器对象来组织文章内容的,成功获取这些武器对象是对这些文章进行信息抽取的前提,更是以后进行信息整合的基础。军备情报说明文的武器对象判定系统模拟人们在日常阅读中的智力活动,通过标题分析、介引句分析和统计判定,实现了武器对象判定。对此进行了论述。     

基于条件随机场的中文人名识别

中文人名数量众多,规律各异,使得中文人名识别成为中文分词的重点和难点,中文人名识别技术的突破将对提高汉语自动分词和句法分析的准确性有着很重要的作用。本文根据中文人名的特点,利用条件随机场模型进行人名识别,通过第四届Bakeoff评测语料上测试,召回率为80.97%,准确率为94.42%,综合指标F为87.18%。     

可交换随机变量的一个结果

本文将独立同分布的结果进行推广,得到了可交换随机变量的收敛性的一个结果．