| copyright | lastupdated | subcollection | ||
|---|---|---|---|---|
|
2024-10-17 |
natural-language-understanding |
{:shortdesc: .shortdesc} {:external: target="_blank" .external} {:deprecated: .deprecated} {:important: .important} {:note: .note} {:tip: .tip} {:preview: .preview} {:beta: .beta} {:pre: .pre} {:codeblock: .codeblock} {:screen: .screen} {:shortdesc: .shortdesc} {:javascript: .ph data-hd-programlang='javascript'} {:java: .ph data-hd-programlang='java'} {:python: .ph data-hd-programlang='python'} {:swift: .ph data-hd-programlang='swift'} {:download: .download}
{: #about}
With {{site.data.keyword.nlufull}}, developers can analyze semantic features of text input, including categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment. {: shortdesc}
{: #features}
Send requests to the API with text, HTML, or a public URL, and specify one or more of the following features to analyze:
{: #categories}
Categorize your content using a five-level classification hierarchy. View the complete list of categories here. For example:
Input
url: "www.cnn.com"
Response
/news
/art and entertainment
/movies and tv/television
/news
/international news
{: #concepts}
Identify high-level concepts that aren't necessarily directly referenced in the text. For example:
Input
text: "Natural Language Understanding uses natural language processing to analyze text."
Response
Linguistics
Natural language processing
Natural language understanding
{: #emotion}
Analyze emotion conveyed by specific target phrases or by the document as a whole. You can also enable emotion analysis for entities and keywords that are automatically detected by the service. For example:
Input
text: "I love apples, but I hate oranges."
targets: "apples", and "oranges"
Response
"apples": joy
"oranges": anger
{: #entities}
Find people, places, events, and other types of entities mentioned in your content. View the complete list of entity types and subtypes here. For example:
Input
text: "IBM is an American multinational technology company headquartered in Armonk, New York, United States, with operations in over 170 countries."
Response
IBM: Company
Armonk: Location
New York: Location
United States: Location
{: #keywords}
Search your content for relevant keywords. For example:
Input
url: "http://www-03.ibm.com/press/us/en/pressrelease/51493.wss"
Response
Australian Open
Tennis Australia
IBM SlamTracker analytics
{: #metadata}
For HTML and URL input, get the author of the webpage, the page title, and the publication date. For example:
Input
url: "https://www.ibm.com/blogs/think/2017/01/cognitive-grid/"
Response
Author: Stephen Callahan
Title: Girding the Grid with Cognitive Computing - THINK Blog
Publication date: January 31, 2017
{: #relations}
Recognize when two entities are related, and identify the type of relation. For example:
Input
text: "The Nobel Prize in Physics 1921 was awarded to Albert Einstein."
Response
"awardedTo" relation between "Noble Prize in Physics" and "Albert Einstein"
"timeOf" relation between "1921" and "awarded"
{: #semantic-roles}
Parse sentences into subject-action-object form, and identify entities and keywords that are subjects or objects of an action. For example:
Input
text: "In 2011, Watson competed on Jeopardy!"
Response
Subject: Watson
Action: competed
Object: on Jeopardy
{: #sentiment}
Analyze the sentiment toward specific target phrases and the sentiment of the document as a whole. You can also get sentiment information for detected entities and keywords by enabling the sentiment option for those features. For example:
Input
text: "Thank you and have a nice day!"
Response
Positive sentiment (score: 0.91)
{: #syntax}
Identify the sentences and tokens in your text. For example:
Input
text: "I love apples! I do not like oranges."
Response
| Sentence | Location |
|---|---|
| "I love apples!" | [0, 14] |
| "I do not like oranges." | [15,37] |
| Token | Lemma | Part of Speech | Location |
|---|---|---|---|
| "I" | "I" | PRON |
[0, 1] |
| "love" | "love" | VERB |
[2, 6] |
| "apples" | "apple" | NOUN |
[7, 13] |
| "!" | PUNCT |
[13, 14] |
|
| "I" | "I" | PRON |
[15, 16] |
| "do" | "do" | AUX |
[17, 19] |
| "not" | "not" | PART |
[20, 23] |
| "like" | "like" | VERB |
[24, 28] |
| "oranges" | "orange" | NOUN |
[29, 36] |
| "." | NOUN |
[36, 37] |
{: #supported-languages}
See the Language support documentation for details about supported languages in {{site.data.keyword.nlushort}}.