"description":"Provides natural language understanding technologies to developers. Examples include sentiment analysis, entity recognition, entity sentiment analysis, and text annotations.",
"description":"The request message for the text annotation API, which can perform multiple\nanalysis types (sentiment, entities, and syntax) in one call.",
"If `EncodingType` is not specified, encoding-dependent information (such as\n`begin_offset`) will be set at `-1`.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-8 encoding of the input. C++ and Go are examples of languages\nthat use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-16 encoding of the input. Java and Javascript are examples of\nlanguages that use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-32 encoding of the input. Python is an example of a language\nthat uses this encoding natively."
"description":"The encoding type used by the API to calculate sentence offsets for the\nsentence sentiment.",
"type":"string",
"enumDescriptions":[
"If `EncodingType` is not specified, encoding-dependent information (such as\n`begin_offset`) will be set at `-1`.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-8 encoding of the input. C++ and Go are examples of languages\nthat use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-16 encoding of the input. Java and Javascript are examples of\nlanguages that use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-32 encoding of the input. Python is an example of a language\nthat uses this encoding natively."
"description":"The language of the text, which will be the same as the language specified\nin the request or, if not specified, the automatically-detected language.\nSee Document.language field for more details.",
"type":"string"
},
"sentences":{
"description":"Sentences in the input document. Populated if the user enables\nAnnotateTextRequest.Features.extract_syntax.",
"description":"Tokens, along with their syntactic information, in the input document.\nPopulated if the user enables\nAnnotateTextRequest.Features.extract_syntax.",
"description":"Entities, along with their semantic information, in the input document.\nPopulated if the user enables\nAnnotateTextRequest.Features.extract_entities.",
"description":"Represents the head of this token in the dependency tree.\nThis is the index of the token which has an arc going to this token.\nThe index is the position of the token in the array of tokens returned\nby the API method. If this token is a root token, then the\n`head_token_index` is its own index.",
"description":"The API calculates the beginning offset of the content in the original\ndocument according to the EncodingType specified in the API request.",
"description":"The `Status` type defines a logical error model that is suitable for different\nprogramming environments, including REST APIs and RPC APIs. It is used by\n[gRPC](https://github.com/grpc). The error model is designed to be:\n\n- Simple to use and understand for most users\n- Flexible enough to meet unexpected needs\n\n# Overview\n\nThe `Status` message contains three pieces of data: error code, error message,\nand error details. The error code should be an enum value of\ngoogle.rpc.Code, but it may accept additional error codes if needed. The\nerror message should be a developer-facing English message that helps\ndevelopers *understand* and *resolve* the error. If a localized user-facing\nerror message is needed, put the localized message in the error details or\nlocalize it in the client. The optional error details may contain arbitrary\ninformation about the error. There is a predefined set of error detail types\nin the package `google.rpc` that can be used for common error conditions.\n\n# Language mapping\n\nThe `Status` message is the logical representation of the error model, but it\nis not necessarily the actual wire format. When the `Status` message is\nexposed in different client libraries and different wire protocols, it can be\nmapped differently. For example, it will likely be mapped to some exceptions\nin Java, but more likely mapped to some error codes in C.\n\n# Other uses\n\nThe error model and the `Status` message can be used in a variety of\nenvironments, either with or without APIs, to provide a\nconsistent developer experience across different environments.\n\nExample uses of this error model include:\n\n- Partial errors. If a service needs to return partial errors to the client,\n it may embed the `Status` in the normal response to indicate the partial\n errors.\n\n- Workflow errors. A typical workflow has multiple steps. Each step may\n have a `Status` message for error reporting.\n\n- Batch operations. If a client uses batch request and batch response, the\n `Status` message should be used directly inside batch response, one for\n each error sub-response.\n\n- Asynchronous operations. If an API call embeds asynchronous operation\n results in its response, the status of those operations should be\n represented directly using the `Status` message.\n\n- Logging. If some API errors are stored in logs, the message `Status` could\n be used directly after any stripping needed for security/privacy reasons.",
"description":"A developer-facing error message, which should be in English. Any\nuser-facing error message should be localized and sent in the\ngoogle.rpc.Status.details field, or localized by the client.",
"description":"All available features for sentiment, syntax, and semantic analysis.\nSetting each one to true will enable that specific analysis for the input.",
"description":"For calls to AnalyzeEntitySentiment or if\nAnnotateTextRequest.Features.extract_entity_sentiment is set to\ntrue, this field will contain the sentiment expressed for this mention of\nthe entity in the provided document."
"description":"Represents a mention for an entity in the text. Currently, proper noun\nmentions are supported.",
"type":"object"
},
"Sentence":{
"properties":{
"sentiment":{
"$ref":"Sentiment",
"description":"For calls to AnalyzeSentiment or if\nAnnotateTextRequest.Features.extract_document_sentiment is set to\ntrue, this field will contain the sentiment for the sentence."
},
"text":{
"description":"The sentence text.",
"$ref":"TextSpan"
}
},
"id":"Sentence",
"description":"Represents a sentence in the input document.",
"description":"The Google Cloud Storage URI where the file content is located.\nThis URI must be of the form: gs://bucket_name/object_name. For more\ndetails, see https://cloud.google.com/storage/docs/reference-uris.\nNOTE: Cloud Storage object versioning is not supported.",
"description":"The language of the document (if not specified, the language is\nautomatically detected). Both ISO and BCP-47 language codes are\naccepted.\u003cbr\u003e\n[Language Support](/natural-language/docs/languages)\nlists currently supported languages for each API method.\nIf the language (either specified by the caller or automatically detected)\nis not supported by the called API method, an `INVALID_ARGUMENT` error\nis returned.",
"description":"The encoding type used by the API to calculate offsets.",
"type":"string",
"enumDescriptions":[
"If `EncodingType` is not specified, encoding-dependent information (such as\n`begin_offset`) will be set at `-1`.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-8 encoding of the input. C++ and Go are examples of languages\nthat use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-16 encoding of the input. Java and Javascript are examples of\nlanguages that use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-32 encoding of the input. Python is an example of a language\nthat uses this encoding natively."
"description":"A non-negative number in the [0, +inf) range, which represents\nthe absolute magnitude of sentiment regardless of score (positive or\nnegative).",
"description":"The recognized entities in the input document with associated sentiments.",
"type":"array",
"items":{
"$ref":"Entity"
}
},
"language":{
"description":"The language of the text, which will be the same as the language specified\nin the request or, if not specified, the automatically-detected language.\nSee Document.language field for more details.",
"description":"The encoding type used by the API to calculate offsets.",
"type":"string",
"enumDescriptions":[
"If `EncodingType` is not specified, encoding-dependent information (such as\n`begin_offset`) will be set at `-1`.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-8 encoding of the input. C++ and Go are examples of languages\nthat use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-16 encoding of the input. Java and Javascript are examples of\nlanguages that use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-32 encoding of the input. Python is an example of a language\nthat uses this encoding natively."
"If `EncodingType` is not specified, encoding-dependent information (such as\n`begin_offset`) will be set at `-1`.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-8 encoding of the input. C++ and Go are examples of languages\nthat use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-16 encoding of the input. Java and Javascript are examples of\nlanguages that use this encoding natively.",
"Encoding-dependent information (such as `begin_offset`) is calculated based\non the UTF-32 encoding of the input. Python is an example of a language\nthat uses this encoding natively."
"description":"The overall sentiment of the input document."
},
"language":{
"description":"The language of the text, which will be the same as the language specified\nin the request or, if not specified, the automatically-detected language.\nSee Document.language field for more details.",
"type":"string"
},
"sentences":{
"description":"The sentiment for all the sentences in the document.",
"description":"The language of the text, which will be the same as the language specified\nin the request or, if not specified, the automatically-detected language.\nSee Document.language field for more details.",
"description":"Represents a phrase in the text that is a known entity, such as\na person, an organization, or location. The API associates information, such\nas salience and mentions, with entities.",
"type":"object",
"properties":{
"mentions":{
"description":"The mentions of this entity in the input document. The API currently\nsupports proper noun mentions.",
"type":"array",
"items":{
"$ref":"EntityMention"
}
},
"name":{
"description":"The representative name for the entity.",
"type":"string"
},
"type":{
"enum":[
"UNKNOWN",
"PERSON",
"LOCATION",
"ORGANIZATION",
"EVENT",
"WORK_OF_ART",
"CONSUMER_GOOD",
"OTHER"
],
"description":"The entity type.",
"type":"string",
"enumDescriptions":[
"Unknown",
"Person",
"Location",
"Organization",
"Event",
"Work of art",
"Consumer goods",
"Other types"
]
},
"metadata":{
"additionalProperties":{
"type":"string"
},
"description":"Metadata associated with the entity.\n\nCurrently, Wikipedia URLs and Knowledge Graph MIDs are provided, if\navailable. The associated keys are \"wikipedia_url\" and \"mid\", respectively.",
"type":"object"
},
"salience":{
"description":"The salience score associated with the entity in the [0, 1.0] range.\n\nThe salience score for an entity provides information about the\nimportance or centrality of that entity to the entire document text.\nScores closer to 0 are less salient, while scores closer to 1.0 are highly\nsalient.",
"format":"float",
"type":"number"
},
"sentiment":{
"description":"For calls to AnalyzeEntitySentiment or if\nAnnotateTextRequest.Features.extract_entity_sentiment is set to\ntrue, this field will contain the aggregate sentiment expressed for this\nentity in the provided document.",
"$ref":"Sentiment"
}
},
"id":"Entity"
},
"AnalyzeSyntaxResponse":{
"properties":{
"language":{
"description":"The language of the text, which will be the same as the language specified\nin the request or, if not specified, the automatically-detected language.\nSee Document.language field for more details.",
"type":"string"
},
"sentences":{
"description":"Sentences in the input document.",
"type":"array",
"items":{
"$ref":"Sentence"
}
},
"tokens":{
"description":"Tokens, along with their syntactic information, in the input document.",
"description":"Finds named entities (currently proper names and common nouns) in the text\nalong with entity types, salience, mentions for each entity, and\nother properties.",
"description":"Analyzes the syntax of the text and provides sentence boundaries and\ntokenization along with part of speech tags, dependency trees, and other\nproperties.",
"description":"API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.",
"description":"Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.",
