我正在使用Google的自然语言analyzeEntities
api,在响应中,有一个嵌套的EntityMention.TextSpan
对象,有2个字段:content和beginOffset。我想利用beginOffset进行进一步的分析。所以我试图映射原始文本中的单词索引并将其与 beginOffset 进行比较,但我注意到索引不同。
我正在使用一种相当幼稚的方法来构建此索引:
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
我从分析实体 api 得到的结果是:
gcloud ml language analyze-entities --content="it will cost you $350 - $600,. test. Alexander. How are you?"
{
"entities": [
{
"mentions": [
{
"text": {
"beginOffset": 23,
"content": "test"
},
"type": "COMMON"
}
],
"metadata": {},
"name": "test",
"salience": 0.7828024,
"type": "OTHER"
},
{
"mentions": [
{
"text": {
"beginOffset": 29,
"content": "Alexander"
},
"type": "PROPER"
}
],
"metadata": {},
"name": "Alexander",
"salience": 0.2171976,
"type": "PERSON"
}
],
"language": "en"
}
我知道非字母数字字符具有特殊的含义和处理方式,我希望偏移量代表真正的索引。
因为,不是用于解析查询文本的规则是什么以及如何计算 beginOffset ?
谢谢!
您可以控制请求中的编码(用于计算偏移量(。(编码类型:https://cloud.google.com/natural-language/docs/analyzing-entities#language-entities-string-protocol(。对于python,您需要将其设置为UTF32(https://cloud.google.com/natural-language/docs/reference/rest/v1/EncodingType(。gcloud 使用 UTF-8 编码,基本上为您提供字节级偏移量。
看起来$
符号是这里的问题。
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": "$600",
"salience": 0.0,
"type": "PRICE"
},
{
"mentions": [
{
"text": {
"beginOffset": 18,
"content": "350"
},
"type": "TYPE_UNKNOWN"
}
],
"metadata": {
"value": "350"
},
"name": "350",
"salience": 0.0,
"type": "NUMBER"
},
{
"mentions": [
{
"text": {
"beginOffset": 25,
"content": "600"
},
"type": "TYPE_UNKNOWN"
}
],
"metadata": {
"value": "600"
},
"name": "600",
"salience": 0.0,
"type": "NUMBER"
}
],
"language": "en"
}
如果您将$
符号更改为#
它似乎按预期工作。
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"
}