     /**
      Analyze text, HTML, or a public webpage.
 
-     Analyzes text, HTML, or a public webpage with one or more text analysis features.
-
-     - parameter parameters: An object containing request parameters. The `features` object and one of the `text`, `html`, or `url` attributes are required.
+     Analyzes text, HTML, or a public webpage with one or more text analysis features.  ### Concepts Identify general
+     concepts that are referenced or alluded to in your content. Concepts that are detected typically have an associated
+     link to a DBpedia resource.  ### Emotion Detect anger, disgust, fear, joy, or sadness that is conveyed by your
+     content. Emotion information can be returned for detected entities, keywords, or user-specified target phrases
+     found in the text.  ### Entities Detect important people, places, geopolitical entities and other types of entities
+     in your content. Entity detection recognizes consecutive coreferences of each entity. For example, analysis of the
+     following text would count \"Barack Obama\" and \"He\" as the same entity:  \"Barack Obama was the 44th President
+     of the United States. He took office in January 2009.\"  ### Keywords Determine the most important keywords in your
+     content. Keyword phrases are organized by relevance in the results.  ### Metadata Get author information,
+     publication date, and the title of your text/HTML content.  ### Relations Recognize when two entities are related,
+     and identify the type of relation.  For example, you can identify an \"awardedTo\" relation between an award and
+     its recipient.  ### Semantic Roles Parse sentences into subject-action-object form, and identify entities and
+     keywords that are subjects or objects of an action.  ### Sentiment Determine whether your content conveys postive
+     or negative sentiment. Sentiment information can be returned for detected entities, keywords, or user-specified
+     target phrases found in the text.   ### Categories Categorize your content into a hierarchical 5-level taxonomy.
+     For example, \"Leonardo DiCaprio won an Oscar\" returns \"/art and entertainment/movies and tv/movies\" as the most
+     confident classification.
+
+     - parameter parameters: An object containing request parameters. The `features` object and one of the `text`, `html`, or `url` attributes
+     are required.
      - parameter failure: A function executed if an error occurs.
      - parameter success: A function executed with the successful result.
-    */
+     */
     public func analyze(
         parameters: Parameters,
         failure: ((Error) -> Void)? = nil,

import Foundation

/**
   Analyze various features of text content at scale. Provide text, raw HTML, or a public URL, and
  IBM Watson Natural Language Understanding will give you results for the features you request. The
  service cleans HTML content before analysis by default, so the results can ignore most
  advertisements and other unwanted content.

  ### Concepts
  Identify general concepts that are referenced or alluded to in your content. Concepts that are
  detected typically have an associated link to a DBpedia resource.

  ### Entities
  Detect important people, places, geopolitical entities and other types of entities in your
  content. Entity detection recognizes consecutive coreferences of each entity. For example,
  analysis of the following text would count "Barack Obama" and "He" as the same entity:

  "Barack Obama was the 44th President of the United States. He took office in January 2009."

  ### Keywords
  Determine the most important keywords in your content. Keyword phrases are organized by relevance
  in the results.

  ### Categories
  Categorize your content into a hierarchical 5-level taxonomy. For example, "Leonardo DiCaprio won
  an Oscar" returns "/art and entertainment/movies and tv/movies" as the most confident
  classification.

  ### Sentiment
  Determine whether your content conveys postive or negative sentiment. Sentiment information can be
  returned for detected entities, keywords, or user-specified target phrases found in the text.

  ### Emotion
  Detect anger, disgust, fear, joy, or sadness that is conveyed by your content. Emotion information
  can be returned for detected entities, keywords, or user-specified target phrases found in the
  text.

  ### Relations
  Recognize when two entities are related, and identify the type of relation.  For example, you can
  identify an "awardedTo" relation between an award and its recipient.

  ### Semantic Roles
  Parse sentences into subject-action-object form, and identify entities and keywords that are
  subjects or objects of an action.

  ### Metadata
  Get author information, publication date, and the title of your text/HTML content.
 */
public class NaturalLanguageUnderstanding {


     - parameter username: The username used to authenticate with the service.
     - parameter password: The password used to authenticate with the service.
     - parameter version: The release date of the version of the API to use. Specify the date
     in "YYYY-MM-DD" format.
     */
    public init(username: String, password: String, version: String) {
        self.credentials = .basicAuthentication(username: username, password: password)
