Merge pull request #923 from watson-developer-cloud/codegen/natural-language-understanding

[Natural Language Understanding] Add newest generated code

Author: lpatino10

natural-language-understanding/src/main/java/com/ibm/watson/developer_cloud/natural_language_understanding/v1/NaturalLanguageUnderstanding.java

@@ -30,42 +30,10 @@
  * 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.
+ * You can create <a target="_blank"
+ * href="https://www.ibm.com/watson/developercloud/doc/natural-language-understanding/customizing.html">custom
+ * models</a> with Watson Knowledge Studio that can be used to detect custom entities and relations in Natural Language
+ * Understanding.
  *
  * @version v1
  * @see <a href="http://www.ibm.com/watson/developercloud/natural-language-understanding.html">Natural Language
@@ -111,7 +79,23 @@ public NaturalLanguageUnderstanding(String versionDate, String username, String
   /**
    * Analyze text, HTML, or a public webpage.
    *
-   * Analyzes text, HTML, or a public webpage with one or more text analysis features.
+   * 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.
    *
    * @param analyzeOptions the {@link AnalyzeOptions} containing the options for the call
    * @return a {@link ServiceCall} with a response type of {@link AnalysisResults}