如何使用机器学习提取公司债券信息 [英] How to extract corporate bonds informations using machine learning

问题描述

我正在一个项目中，我需要从非结构化电子邮件中提取公司债券信息.经过大量研究，我发现机器学习可用于信息提取.我尝试使用Opennlp NER(命名实体识别器)，但不确定是否为此问题选择了正确的库，因为我得到的是结果，但未达到要求.

I am working on a project where I need to extract corporate bonds information from the unstructured emails. After doing a lot of research, I found that machine learning can be used for information extraction. I tried Opennlp NER (Named entity recognizer) but I am not sure whether I picked up the correct library for this problem or not because I am getting the results but not up to the mark.

有人可以向我建议任何库或算法吗，这意味着我该如何解析和提取数据.我打算探索朴素贝叶斯或N-gram或支持向量机，但不确定，这对我有没有帮助.请提出建议.

Could someone please suggest me any library or algorithms means how can I parse and extract data from it. I am planning to explore Naïve Bayes or N-gram or Support vector machine but not sure, this will help me or not. Please suggest.

示例如下:

[/] Trading 10mm ABC 2.5 19 05/06 mkt can use 50mm --->在这里我要提取"ABC 2.5 19"

[/] Trading 10mm ABC 2.5 19 05/06 mkt can use 50mm ---> here I want to extract "ABC 2.5 19"

示例2:

XYZ 6.5 15 10-2B 106-107 B3 AAA- 1.646MM 2x2 --->在这里我要提取"XYZ 6.5 15"

XYZ 6.5 15 10-2B 106-107 B3 AAA- 1.646MM 2x2 ---> here I want to extract "XYZ 6.5 15"

推荐答案

在Perl中，您可以使用 Marpa: :R2 —通用的BNF解析器.

In Perl, you can use Marpa::R2 — a general BNF parser.

此要点从您的示例中提取信息.

This gist extracts info from your examples.

希望这会有所帮助.

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