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属性抽取调研-工业界

• 人工智能 • 阅读 106

1. 任务

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属性抽取(Attribute Extraction)：属性抽取的目标是从不同信息源中采集特定实体的属性信息。比如人物实体的生日、性别、国籍等，都是它的属性信息，通过属性抽取，通过多个数据源的获取，我们就可以通过丰富的属性信息来较为完整地刻画一个实体。

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  • Accuracy
  • Precision
  • f1

2. 方法总结

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* 方法：通过人工编写规则针对人物场景进行属性抽取，由于人工构造规则模板比较麻烦，可以使用Bootstrapping方法生成规则。 生成规则的步骤如下： 1、人工置顶规则种子作为初始规则种子集Spatter，属性值集合Sattr 2、使用规则种子集Spatter，遍历并匹配文本中的属性值，获取候选属性集合h 3、计算候选属性值集合h中每个属性值的可行度，将可信度较高的三个属性值加入到种子属性值集合Sattr中，若收敛，则算法结束，否则，执行4 4、使用属性值集合Sattr，遍历文本，由匹配到的属性值的上下文两个词，生成候选模板集合h’ 5、计算候选模板集合h’中每个候选模板的可信度，将可信度较高的3个候选模板加入到规则种子集合Spatter中，若Spatter收敛，则算法结束，否则执行步骤2 重复2-5到满足一定的次数。
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* 场景：产品的属性抽取
* 论文：《An Unsupervised Approach to Product Attribute Extraction》
* 方法： 1、数据预处理： 找出限定性的短语和名词短语，论文认为一般属性出现在这种词语中 2、对上一步筛选出的名词进行聚类，删除词语稍少的类 3、从类中抽取属性：计算unigrams, bigrams and trigrams，使用作者定义的属性分数函数进行计算，得分高的则为属性。

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* 方法： 依存关系：在自然语言处理中，用词与词之间的依存关系来描述语言结构的框架称为依存语法(dependence grammar)，又称从属关系语法。利用依存句法进行句法分析也是自然语言理解的重要技术之一。(来自维基百科)。 使用这种方法对人物进行属性抽取的步骤如下: 1、为每个属性生成对应的触发词表 2、根据属性槽特点，识别出句子中所有可能候选属性，比如出生地的NER标注为LOC，感觉是自己设置一些规则匹配一些属性出来 3、通过句子的依存结构，确认侯选属性与主体实体(这里是人物)的关系。将依存关系树看作一个无向图，顶点对应pagerank算法中的网页，利用pagerank算法，计算两个词在句法上的相关性。 4、计算
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* 论文：《基于弱监督的属性关系抽取方法》《面向非结构化文本的开放式实体属性抽取》《实体 —属性抽取的GRU+CRF方法》《Personal Attributes Extraction in Chinese Text Based on Distant-Supervision and LSTM》《Bootstrapped Named Entity Recognition for Product Attribute Extraction》等论文中都使用了这种方法进行抽取
* 方法：将属性抽取看作序列标注问题，标注需要花费一定的人工成本，在有些场景下，比如人物属性的抽取，可以使用百度百科等百科词条的结构化信息框进行标注，可以降低一定的人工标注成本；同时，标注时也可以使用Bootstrap方法由种子发现更多潜在属性值，这种方法在《Bootstrapped Named Entity Recognition for Product Attribute Extraction》论文中提到，是一种类似于Pakhomov 2002提出的首字母扩写算法的算法。该算法学习如何将首字母缩写与其正确扩展相关联的上下文，作者认为，分类器在经过标记的已知品牌训练集上进行训练，可以学习可以区分当前含义的上下文模式。序列标注常使用的模型：CRF模型、神经网络模型如BI-GRU+CRF模型等。
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* 论文：《MetaPAD-Meta Pattern Discovery from Massive Text Corpora》
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3. Paper List

会议/年份论文链接ACM SIGKDD International Conference 2017MetaPAD-Meta Pattern Discovery from Massive Text Corpora

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4. 相关链接

5. 参考资源

Original: https://blog.csdn.net/ox180x/article/details/124095596
Author: ox180x
Title: 属性抽取调研-工业界

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