Question

我使用的是Google的自然语言analyzeEntities api，响应中有一个嵌套的EntityMention.TextSpan对象，其中包含2个字段：content和beginOffset。我想利用beginOffset进行一些进一步的分析。因此，我试图映射原始文本中的单词索引，并将其与beginOffset进行比较，但是我注意到索引是不同的。

我正在使用一种相当幼稚的方法来建立该索引：

const msg = "it will cost you $350 - $600,. test. Alexander. How are you?"
let index = 0
msg.split(" ").forEach(part => {
  console.log(part + ":"  + index)
  index = index + part.length + 1 // + 1 for the split on space
})

结果是：

it:0
will:3
cost:8
you:13
$350:17
-:22
$600,.:24
test.:31
Alexander.:37
How:48
are:52
you?:56

我从analyticsEntities api获得的结果是：

gcloud ml language analyze-entities --content="it will cost you $350 - $600,. test. Alexander. How are you?"                
{
  "entities": [
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 23,
            "content": "test"
          },
          "type": "COMMON"
        }
      ],
      "metadata": {},
      "name": "test",
      "salience": 0.7828024,
      "type": "OTHER"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 29,
            "content": "Alexander"
          },
          "type": "PROPER"
        }
      ],
      "metadata": {},
      "name": "Alexander",
      "salience": 0.2171976,
      "type": "PERSON"
    }
  ],
  "language": "en"
}

我了解非字母数字字符具有特殊含义和处理方式，我希望偏移量能够代表真实的索引。

既然如此，不是解析查询文本所用的规则是什么，beginOffset是如何计算的？

谢谢！

Answer 1

您可以控制请求中的编码（用于计算偏移量）。（encodingType：https://cloud.google.com/natural-language/docs/analyzing-entities#language-entities-string-protocol）。对于python，您需要将其设置为UTF32（https://cloud.google.com/natural-language/docs/reference/rest/v1/EncodingType）。 gcloud使用的是UTF-8编码，基本上可以为您提供字节级的偏移量。

Answer 2

看起来$是这里的问题。

gcloud ml language analyze-entities --content="it will cost you \$350 - \$600,. test. Alexander. How are you?" 
{
  "entities": [
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 31,
            "content": "test"
          },
          "type": "COMMON"
        }
      ],
      "metadata": {},
      "name": "test",
      "salience": 0.7828024,
      "type": "OTHER"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 37,
            "content": "Alexander"
          },
          "type": "PROPER"
        }
      ],
      "metadata": {},
      "name": "Alexander",
      "salience": 0.2171976,
      "type": "PERSON"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 17,
            "content": "$350"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "currency": "USD",
        "value": "350.000000"
      },
      "name": "$350",
      "salience": 0.0,
      "type": "PRICE"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 24,
            "content": "$600"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "currency": "USD",
        "value": "600.000000"
      },
      "name": "$600",
      "salience": 0.0,
      "type": "PRICE"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 18,
            "content": "350"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "value": "350"
      },
      "name": "350",
      "salience": 0.0,
      "type": "NUMBER"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 25,
            "content": "600"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "value": "600"
      },
      "name": "600",
      "salience": 0.0,
      "type": "NUMBER"
    }
  ],
  "language": "en"
}

如果将$的符号更改为#，它似乎可以正常工作。

gcloud ml language analyze-entities --content="it will cost you #350 - #600,. test. Alexander. How are you?" 
{
  "entities": [
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 31,
            "content": "test"
          },
          "type": "COMMON"
        }
      ],
      "metadata": {},
      "name": "test",
      "salience": 0.9085014,
      "type": "OTHER"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 37,
            "content": "Alexander"
          },
          "type": "PROPER"
        }
      ],
      "metadata": {},
      "name": "Alexander",
      "salience": 0.09149864,
      "type": "PERSON"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 18,
            "content": "350"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "value": "350"
      },
      "name": "350",
      "salience": 0.0,
      "type": "NUMBER"
    },
    {
      "mentions": [
        {
          "text": {
            "beginOffset": 25,
            "content": "600"
          },
          "type": "TYPE_UNKNOWN"
        }
      ],
      "metadata": {
        "value": "600"
      },
      "name": "600",
      "salience": 0.0,
      "type": "NUMBER"
    }
  ],
  "language": "en"
}

如何针对Google自然语言实体api响应计算beginoffset？

2 个答案: