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Merged
amygdala merged 5 commits into from
Feb 16, 2018
Merged

fix merge glitch #14

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7aa0ec1
checkpoint
amygdala Feb 15, 2018
59925f4
checkpointing, add links to enable APIs
amygdala Feb 16, 2018
e9f701c
links to enable apis
amygdala Feb 16, 2018
93b4739
Merge branch 'master' into neos4
amygdala Feb 16, 2018
60797db
fixing merge glitch
amygdala Feb 16, 2018
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114 changes: 80 additions & 34 deletions ml/cloud_shell_tutorials/cloud-nl-intro/tutorial.md
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ The beginning of your response should look like this:
"mid": "/m/042xh",
"wikipedia_url": "https://en.wikipedia.org/wiki/J._K._Rowling"
},
"salience": 0.7980405,
"salience": 0.78821284,
"mentions": [
{
"text": {
Expand Down Expand Up @@ -151,7 +151,7 @@ The beginning of your response should look like this:
```

For each entity in the response, we get the entity `type`, the associated Wikipedia URL if there is one, the
`salience`, and the indices of where this entity appeared in the text. Salience is a number in the [0,1] range that refers to the centrality of the entity to the text as a whole. The Natural Language API can also recognize the same entity mentioned in different ways. Take a look at the `mentions` list in the response: ​the API is able to tell that "Joanne Rowling", "Rowling", "novelist" and "Robert Galbriath" all point to the same thing.​
`salience`, and the indices of where this entity appeared in the text. Salience is a number in the [0,1] range that refers to the centrality of the entity to the text as a whole. The Natural Language API can also recognize the same entity mentioned in different ways. Take a look at the `mentions` list in the response: ​the API is able to tell that "Joanne Rowling", "Rowling", "novelist" and "Robert Galbraith" all point to the same thing.​

Next, we'll use the Natural Language API to perform sentiment analysis.

Expand Down Expand Up @@ -261,49 +261,49 @@ In the response, you get back two entity objects: one for "sushi" and one for "s
{
"entities": [
{
"name": "sushi",
"type": "CONSUMER_GOOD",
"name": "service",
"type": "OTHER",
"metadata": {},
"salience": 0.52716845,
"salience": 0.5136989,
"mentions": [
{
"text": {
"content": "sushi",
"beginOffset": 12
"content": "service",
"beginOffset": 26
},
"type": "COMMON",
"sentiment": {
"magnitude": 0.9,
"score": 0.9
"score": -0.9
}
}
],
"sentiment": {
"magnitude": 0.9,
"score": 0.9
"score": -0.9
}
},
{
"name": "service",
"type": "OTHER",
"name": "sushi",
"type": "CONSUMER_GOOD",
"metadata": {},
"salience": 0.47283158,
"salience": 0.4863011,
"mentions": [
{
"text": {
"content": "service",
"beginOffset": 26
"content": "sushi",
"beginOffset": 12
},
"type": "COMMON",
"sentiment": {
"magnitude": 0.9,
"score": -0.9
"score": 0.9
}
}
],
"sentiment": {
"magnitude": 0.9,
"score": -0.9
"score": 0.9
}
}
],
Expand Down Expand Up @@ -373,16 +373,12 @@ The response should return an object like the one below for each token in the se
}
```

Let's break down the response:

* `partOfSpeech` tells us that "Joanne" is a noun.
* `dependencyEdge` includes data that you can use to create a [dependency parse tree](https://en.wikipedia.org/wiki/Parse_tree#Dependency-based_parse_trees) of the text. Essentially, this is a diagram showing how words in a sentence relate to each other. A dependency parse tree for the sentence above would look like this:
* `partOfSpeech` tells us that "uses" is a verb.
* `dependencyEdge` includes data that you can use to create a [dependency parse tree](https://en.wikipedia.org/wiki/Parse_tree#Dependency-based_parse_trees) of the text. Essentially, this is a diagram showing how words in a sentence relate to each other. The (first part) of a dependency parse tree for the sentence above looks like this:

![parse tree](https://storage.googleapis.com/aju-dev-demos-codelabs/images/parse_tree.png)
![parse tree](https://storage.googleapis.com/aju-dev-demos-codelabs/images/hermione.png)

* `headTokenIndex` is the index of the token that has an arc pointing at "Joanne". We can think of each token in the sentence as a word in an array.
* `headTokenIndex` of 1 for "Joanne" refers to the word "Rowling," which it is connected to in the tree. The label `NN` (short for noun compound modifier) describes the word's role in the sentence. "Joanne" modifies "Rowling," the subject of the sentence.
* `lemma` is the canonical form of the word. For example, the words *run* , *runs* , *ran* , and *running* all have a lemma of *run* . The lemma value is useful for tracking occurrences of a word in a large piece of text over time.
* `lemma` is the canonical form of the word. The lemma value is useful for tracking occurrences of a word in a large piece of text over time.

The Natural Language API also supports languages other than English. Let's look at a Japanese example next.

Expand All @@ -406,6 +402,7 @@ It should look like this:
}
```

The text can translate in English as: "Google's office in Japan is in Roppongi Hills, Tokyo".
Notice that you didn't need to tell the API which language the text is — it can automatically detect it!

Next, you'll send this request to the `analyzeEntities` endpoint:
Expand All @@ -424,15 +421,15 @@ You should get the following response:
"name": "日本",
"type": "LOCATION",
"metadata": {
"mid": "/m/03_3d",
"wikipedia_url": "https://en.wikipedia.org/wiki/Japan"
"wikipedia_url": "https://en.wikipedia.org/wiki/Japan",
"mid": "/m/03_3d"
},
"salience": 0.23854347,
"mentions": [
{
"text": {
"content": "日本",
"beginOffset": 0
"beginOffset": -1
},
"type": "PROPER"
}
Expand All @@ -442,28 +439,77 @@ You should get the following response:
"name": "グーグル",
"type": "ORGANIZATION",
"metadata": {
"mid": "/m/045c7b",
"wikipedia_url": "https://en.wikipedia.org/wiki/Google"
"wikipedia_url": "https://en.wikipedia.org/wiki/Google",
"mid": "/m/045c7b"
},
"salience": 0.21155767,
"mentions": [
{
"text": {
"content": "グーグル",
"beginOffset": 9
"beginOffset": -1
},
"type": "PROPER"
}
]
},
...
]
{
"name": "六本木ヒルズ",
"type": "PERSON",
"metadata": {
"mid": "/m/01r2_k",
"wikipedia_url": "https://en.wikipedia.org/wiki/Roppongi_Hills"
},
"salience": 0.18502845,
"mentions": [
{
"text": {
"content": "六本木ヒルズ",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "東京",
"type": "LOCATION",
"mentions": [
"metadata": {
"mid": "/g/12lnhn10f",
"wikipedia_url": "https://de.wikipedia.org/wiki/Tokio"
},
"salience": 0.18378882,
"mentions": [
{
"text": {
"content": "東京",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "オフィス",
"type": "OTHER",
"metadata": {},
"salience": 0.1810816,
"mentions": [
{
"text": {
"content": "オフィス",
"beginOffset": -1
},
"type": "COMMON"
}
]
}
],
"language": "ja"
}
```

The wikipedia URLs even point to the Japanese Wikipedia pages - so cool!

## Congratulations!

`walkthrough conclusion-trophy`
Expand Down