Titan is designed to understand and analyze textual data related to events, extracting relevant information such as NER, Sentiment Analysis, event descriptions, participants, and locations, enabling automated event comprehension and facilitating decision-making based on event-specific insights.
Event Extraction
Seamlessly categorize text data
with high accuracy.
Text extraction automatically identifies and extracts structured information about events from unstructured or semi-structured data. The goal of event extraction is to identify specific events, the entities involved, and the relationships between those entities.
Event Visualization
Intuitively and easily understand representations of event data.
Text visualization refers to visual representations of events extracted from unstructured or semi-structured data. Event visualization aims to make it easy for humans to interpret and analyze information visually.
Event Compression
Capture the essential aspects of events while reducing redundancy.
Event compression refers to summarizing or reducing a sequence of events into a more compact and concise representation with minimal information loss.
Event Tagging
Assign grammatical labels to words or phrases within a sentence.
Event tagging automatically identifies and labels text, events, and their attributes in natural language data. Event tagging aims to identify the various components of events, such as the actors, actions, and so on, and assign specific labels or tags to each component.
Event Cognition
Enable more advanced search and retrieval of information,
Event cognition is the ability to perceive, understand, and reason about events. It involves identifying and representing events, including their causes, effects, and relationships to other events.
Event Analysis
Classify named entities and extract meaningful insights from textual data.
Event analysis encompasses various techniques. Sentiment analysis focuses on determining the emotional tone associated with events. At the same time, NER aims to identify and classify named entities such as people, organizations, locations, and others.
We've partnered with RapidAPI, the leading API marketplace for software developers, to simplify the implementation of the Titan API service with your software.
Frequently Asked Questions
Do you require a credit card?
A credit card is required only if the plan you choose is a paid plan or has a quota with an overage fee. A credit card is not needed for the free plan.
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When will I be billed?
Your credit card is charged upon subscription to a paid plan and at the next recurring interval.
What if I exceed my plan limits?
Depending on your plan’s specification, you will either incur overage charges or be suspended until next billing cycle.
How are refunds handled?
For refund requests, please contact support@rapidapi.com
How do I unsubscribe from a plan?
If you would no longer like to use the Titan API, you can unsubscribe from the plan at any time by clicking the “unsubscribe” button under the Billing section of the RapidAPI Dashboard.
How do I get started with building applications?
Please review the Titan API reference guide.
How do I submit my feedback?
Fill in the Titan evaluation form to send in your questions and comments. We will address your inquiries as soon as possible.
Documentation
Titan API reference guide (v1.0.1)
The Titan API follows REST principles, allowing you to access data resources through standard HTTPS requests in UTF-8 format to an API endpoint. The following HTTP verb is used by the Titan NLU API.
METHOD
|
ACTION
|
---|---|
PUT
|
Submits text to be analyzed by Titan Agent. Titan Agent responds in the form of JSON
|
The Titan API endpoints always return responses in JSON metadata format and can be accessed through the base address:
https://nlp-suite.p.rapidapi.com/
To consume the Titan API endpoints, you need to authenticate your request using an X-RapidAPI-Key.
Please register to obtain your API key.
The API endpoints for accessing Titan NLP services may vary based on your subscription plan. Therefore, ensure that you use the correct API endpoints.
The Sentiment Analysis endpoint examines the emotional tone or attitude expressed in a text.
SUBSCRIPTION PLANS
|
SENTIMENT ANALYSIS ENDPOINTS
|
---|---|
BASIC
|
/Basic/Sentiments
|
PRO
|
/Pro/Sentiments
|
ULTRA
|
/Ultra/Sentiments
|
MEGA
|
/Mega/Sentiments
|
The POS endpoint identifies different word categories in a language, such as nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections.
SUBSCRIPTION PLANS
|
POS ENDPOINTS
|
---|---|
BASIC
|
/Basic/POS
|
PRO
|
/Pro/POS
|
ULTRA
|
/Ultra/POS
|
MEGA
|
/Mega/POS
|
The Event Components endpoint extracts various elements that make up an event, such as the date, time, location, actors, and activities involved.
SUBSCRIPTION PLANS
|
EVENT COMPONENTS ENDPOINTS
|
---|---|
BASIC
|
/Basic/Components
|
PRO
|
/Pro/Components
|
ULTRA
|
/Ultra/Components
|
MEGA
|
/Mega/Components
|
The Content Summary endpoint offers a concise overview or synopsis of the main points or ideas in a text.
SUBSCRIPTION PLANS
|
CONTENT SUMMARY ENDPOINTS
|
---|---|
BASIC
|
/Basic/Summary
|
PRO
|
/Pro/Summary
|
ULTRA
|
/Ultra/Summary
|
MEGA
|
/Mega/Summary
|
The Text Reduction endpoint condenses text by removing redundant or irrelevant information.
SUBSCRIPTION PLANS
|
TEXT REDUCTION ENDPOINTS
|
---|---|
BASIC
|
/Basic/Reduction
|
PRO
|
/Pro/Reduction
|
ULTRA
|
/Ultra/Reduction
|
MEGA
|
/Mega/Reduction
|
The Named Entity endpoint associates different word categories in a language, such as nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections.
SUBSCRIPTION PLANS
|
NER ENDPOINTS
|
---|---|
BASIC
|
/Basic/NER
|
PRO
|
/Pro/NER
|
ULTRA
|
/Ultra/NER
|
MEGA
|
/Mega/NER
|
During API requests you will frequently encounter the following JSON object
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
content
|
Enter your content
|
"What color is the sun?"
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
service
|
Service Name
|
ner
|
source
|
Source of the service
|
TitanVX Atlas NLP
|
url
|
URL of the service provider
|
https://www.titanvx.com
|
core_version
|
Version of the platform core
|
C10A
|
agent_version
|
Version of the agent handling the service request
|
C10A.2
|
model_version
|
Version of the NLP model used
|
C10 06.03.2023
|
cloud_version
|
Version of the cloud service hosting the endpoint
|
C10B
|
user_key
|
Returned string key originally sent by the user in the request JSON
|
my_key_23414
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
sentences []
|
Array of Sentiment Sentence objects
|
Sentiment Sentence
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
sentiment
|
Weighted sentiment of the sentence (-1:negative, 1:positive)
|
0.973151
|
text
|
Text string of the sentence
|
American Gangster is a 2007 American biographical...
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
sentences []
|
Array of PoS Sentence objects
|
POS Sentence
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
text
|
Text string of the sentence
|
American Gangster is a 2007 American biographical...
|
tokens []
|
Array of Token PoS objects
|
Token POS
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
text
|
Token text
|
American
|
lemma
|
Token lemma
|
American
|
part_of_speech
|
NNP
|
|
index
|
Token zero-based index in the document
|
00
|
depth
|
Token zero-based depth in the dependency tree
|
2
|
parent
|
Text of parent of this token
|
Gangster
|
parent_index
|
Zero-based index of the parent of this token
|
1
|
dependency
|
COMPOUND
|
|
ner_type
|
Person
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
sentences []
|
Array of Component Sentence objects
|
Component Sentence
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
text
|
Text string of the sentence
|
American Gangster is a 2007 American biographical...
|
components []
|
Array of component objects
|
Component
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
type
|
subject
|
|
text
|
Token text
|
American Gangster
|
start_token
|
Zero-based index of starting token of the component
|
00
|
end_token
|
Zero-based index of ending token of the component
|
1
|
parent_index
|
Zero-based index of parent token of component
|
2
|
tense
|
Tense of the verb if the component type is a verb
|
Present perfect
|
passive
|
True when the verb component is in passive tense; false otherwise
|
false
|
negate
|
True when the verb is negated; false otherwise
|
false
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
text
|
Text string of the summarized document
|
American Gangster is a 2007...
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
sentences []
|
Array of Reduction Sentence objects
|
Reduction Sentence
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
index
|
Index of the reduced sentence in the document
|
1
|
text
|
Reduced text of the sentence
|
American Gangster is a 2007...
|
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
header
|
JSON Header object
|
|
entities []
|
Array of Named Entity objects
|
Named Entity
OBJECTS
|
DESCRIPTION
|
EXAMPLE
|
---|---|---|
name
|
Entity name
|
Frank Lucas
|
type
|
Person
|
|
metadata []
|
Array of Entity MD objects
|
Miscellaneous
|
POS Table
PART OF SPEECH TAGS
|
|
---|---|
TYPE
|
DESCRIPTION
|
ADJ
|
Adjective
|
ADP
|
Adposition
|
ADV
|
Adverb
|
AUX
|
Auxiliary
|
CONJ
|
Conjunction
|
CCONJ
|
Coordinating conjunction
|
DET
|
Determiner
|
INTJ
|
Interjection
|
NOUN
|
Noun
|
NUM
|
Numeral
|
PART
|
Particle
|
PRON
|
Pronoun
|
PROPN
|
Proper noun
|
PUNCT
|
Punctuation
|
SCONJ
|
Subordinating conjunction
|
SYM
|
Symbol
|
VERB
|
Verb
|
X
|
Other
|
EOL
|
End of line
|
SPACE
|
Space
|
PERIOD
|
Punctuation mark, sentence closer
|
COMMA
|
Punctuation mark, comma
|
LRB
|
Left round bracket
|
RRB
|
Right round bracket
|
LEFT_QUOTE
|
Opening quotation mark
|
RINGHT_QUOTE
|
Closing quotation mark
|
APOSTROPHE
|
Closing quotation mark
|
COLON
|
Punctuation mark, colon or ellipsis
|
DOLLAR
|
Symbol currency
|
HASH
|
Symbol number sign
|
AFX
|
Affix
|
CC
|
Conjunction, coordinating
|
CD
|
Cardinal number
|
DT
|
Determiner
|
EX
|
Existential there
|
FW
|
Foreign word
|
HYPH
|
Punctuation mark, hyphen
|
IN
|
Conjunction, subordinating or preposition
|
JJ
|
Adjective (English), other noun-modifier (Chinese)
|
JJR
|
Adjective comparative
|
PRP_D
|
Pronoun possessive
|
JJS
|
Adjective superlative
|
LS
|
List item marker
|
MD
|
Verb modal auxiliary
|
NIL
|
Missing tag
|
NN
|
Noun singular or mass
|
NNP
|
Noun proper singular
|
NNPS
|
Noun proper plural
|
NNS
|
Noun plural
|
NN
|
Noun singular or mass
|
PDT
|
Predeterminer
|
POS
|
Possessive ending
|
PRP
|
Pronoun personal
|
PRP_D
|
Pronoun possessive
|
RB
|
Adverb
|
RBR
|
Adverb comparative
|
RBS
|
Adverb superlative
|
RP
|
Adverb particle
|
TO
|
Infinitival to
|
UH
|
Interjection
|
VB
|
Verb base form
|
VBD
|
Verb past tense
|
VBG
|
Verb gerund or present participl
|
VBN
|
Verb past participle
|
VBP
|
Verb non-3rd person singular present
|
VBZ
|
Verb 3rd person singular present
|
WDT
|
Wh-determiner
|
WP
|
Wh-pronoun, personal
|
WP_DOLLAR
|
Wh-pronoun possessive
|
WRB
|
Wh-adverb
|
SP
|
Space (English), sentence-final particle (Chinese)
|
ADD
|
Email
|
NFP
|
Superfluous punctuation
|
GW
|
Additional word in multi-word expression
|
XX
|
Unknown
|
BES
|
Auxiliary be
|
HVS
|
Forms of have
|
_SP
|
Whitespace 76
|
Dependency Table
DEPENDENCY TREE LABELS
|
|
---|---|
LABEL
|
DESCRIPTION
|
ROOT
|
Root
|
PUNCT
|
Adposition
|
INTJ
|
Interjection
|
MARK
|
Marker
|
PARATAXIS
|
Parataxis
|
PRECONJ
|
Pre-correlative conjunction
|
CC
|
Coordinating conjunction
|
CONJ
|
Conjunct
|
NEG
|
Negation modifier
|
ADVMOD
|
Adverbial modifier
|
COMPOUND
|
Nominal/Cardinal compound
|
NMOD
|
Modifier of nominal
|
PREP
|
Prepositional modifier
|
HYPH
|
Hyphen
|
EXPL
|
Expletive
|
DEP
|
Unclassified dependent
|
QUANTMOD
|
Modifier of quantifier
|
POBJ
|
Object of prepositio
|
PREDET
|
Determiner
|
DET
|
Determiner
|
NUMMOD
|
Numeric modifier
|
NPMOD
|
Noun phrase as adverbial modifier
|
AMOD
|
Adjectival modifier
|
POSSESSIVE
|
Possessive modifier
|
CASE
|
Case marking
|
POSS
|
Possession modifier
|
ACL
|
Clausal modifier of noun (adjectival clause)
|
RELCL
|
Relative clause modifier
|
APPOS
|
Appositional modifier
|
NSUBJ
|
Nominal subject
|
NSUBJPASS
|
Nominal subject (passive)
|
DOBJ
|
Direct object
|
IOBJ
|
Indirect object
|
ACOMP
|
Adjectival complement
|
DATIVE
|
Dative complement (Beneficiary)
|
ATTR
|
Attribute of a copula
|
AGENT
|
Agent
|
OPRD
|
Object predicate
|
CSUBJ
|
Clausal subject
|
CSUBJPASS
|
Clausal subject(passive)
|
CCOMP
|
Clausal complement
|
XCOMP
|
Open clausal complemen
|
AUX
|
Auxiliary
|
AUXPASS
|
Auxiliary(passive)
|
PRT
|
Particle
|
PCOMP
|
Complement of preposition
|
ADVCL
|
Adverbial clause modifier
|
NPADVMOD
|
Noun phrase as adverbial modifier
|
DIR
|
Direction (from/to)
|
CLF
|
Classifier
|
COMPLM
|
Complementizer
|
COP
|
Copula
|
DISCOURSE
|
Discourse element
|
DISLOCATE
|
Dislocated elements
|
FIXED
|
Fixed multi-word expression
|
FLAT
|
Flat multi-word expression
|
GOESWITH
|
Goes with 'with out'
|
HMOD
|
Modifier in hyphenation
|
INFMOD
|
Infinitival modifier
|
LIST
|
List
|
META
|
Meta modifier
|
NN
|
Noun compound modifier
|
NOUNMOD
|
Modifier of nominal
|
NUM
|
Number modifier
|
NUMBER
|
Number compound modifier
|
OBJ
|
Object
|
OBL
|
Oblique nominal
|
ORPHAN
|
Orphan
|
PARTMOD
|
Participal modifie
|
RCMOD
|
Relative clause modifier
|
REPARANDUM
|
Overridden disfluency
|
ROOT_S
|
Root
|
VOCATIVE
|
Vocative
|
NER Table
NAMED ENTITY TYPES
|
|
---|---|
TYPE
|
DESCRIPTION
|
Person
|
People, including fictional
|
Nationality/Political group
|
Nationalities or religious or political groups
|
Facility
|
Buildings, airports, highways, bridges, etc.
|
Organization
|
Companies, agencies, institutions, etc.
|
Geographical Entity
|
Countries, cities, states
|
Location
|
GPE locations, mountain ranges, bodies of water
|
Commercial Product
|
Objects, vehicles, foods, etc. (not services
|
Event
|
Named hurricanes, battles, wars, sports events, etc.
|
Art
|
Titles of books, songs, etc.
|
Law
|
Named documents made into laws.
|
Language
|
Any named language
|
Date
|
Absolute or relative dates or period
|
Time
|
Times smaller than a day
|
Percentage
|
Percentage, including "%"
|
Currency
|
Monetary values, including unit
|
Quantity
|
Measurements, as of weight or distance
|
Ordinal
|
First, second, etc.
|
POBJ
|
Object of prepositio
|
Cardinal
|
Numerals that do not fall under another type
|
Frequency
|
Named person or family.
|
Miscellaneous
|
Miscellaneous entities, e.g. events, nationalities, products or works of art
|
Social Event
|
Festivals, cultural events, sports events, weather phenomena, wars, etc.
|
Product
|
Product, i.e. artificially produced entities including speeches, radio shows, programming languages, contracts, laws and ideas
|
Derivative
|
Words (and phrases?) that are derived from a name, but not a name in themselves, e.g. 'Oslo-mannen' ('the man from Oslo')
|
Geopolitical Entity
|
Geo-political entity, with a locative sense, e.g. 'John lives in Spain'
|
Geopolitical Organization
|
Geo-political entity, with an organization sense, e.g. 'Spain declined to meet with Belgium'
|
Components Table
COMPONENT TYPES
|
|
---|---|
TYPE
|
DESCRIPTION
|
Time
|
Temporal component
|
Space
|
Spatial componeny
|
Domain
|
Domain, group, or topic component
|
Manner
|
Manner component or manner clause
|
Time/Space
|
Temporal or spatial component (e.g. "at the wedding")
|
Cause
|
Causal component or causal clause
|
Anti-causal
|
Anti-causal component or clause (e.g. "although X" "even though X")
|
Consequence
|
Consequential component or clause (e.g. "therefore X" "thus X"
|
Correlation
|
Correlation component or clause (e.g. "as X")
|
Condition
|
Conditional component or clause (e.g. "if X")
|
Goal
|
Goal component or clause (e.g. "In order to X" "so that X")
|
Possession
|
Possession of a noun or nominal phrase
|
Particle
|
Particle of a verb
|
Subject
|
Subject of a verb
|
Passive Subject
|
Monetary valuePassive subject of a verb, including unit
|
Clausal Subject
|
Clausal subject of a verb
|
Passive Clausal Subject
|
Passive clausal subject of a verb
|
Clausal Complement
|
Clausal complement of a verb
|
Close Clausal Complement
|
Close clausal complement of a verb
|
Direct Object
|
Direct object of a verb
|
Indirect Object
|
Indirect object of a verb
|
Attribute
|
Attribute of a copula
|
State
|
State of a noun or nominal phrase
|
Agent
|
Agent of a passive verb
|
Verb
|
Verb
|
Auxiliary
|
Auxiliary or tense modifier of a verb
|
To get started easily and quickly, just import the SDK of your choice by copying the provided code snippet into your application (Available at RapidAPI).
STATUS CODE
|
DESCRIPTION
|
---|---|
200
|
OK - The request has succeeded. The client can read the result of the request in the body and the headers of the response.
|
400
|
Bad Request - The request could not be understood by the server due to malformed syntax. The message body will contain more information; see Response Schema.
|
401
|
Unauthorized - The request requires user authentication or, if the request included authorization credentials, authorization has been refused for those credentials.
|
403
|
Forbidden - The server understood the request, but is refusing to fulfill it.
|
404
|
Not Found - The requested resource could not be found. This error can be due to a temporary or permanent condition.
|
429
|
Too Many Requests - Rate limiting has been applied.
|
500
|
Internal Server Error- When this happens try your request again at a later time or contact our support team to get assistance.
|
502
|
Bad Gateway - The server was acting as a gateway or proxy and received an invalid response from the upstream server.
|
503
|
Bad Gateway - The server was acting as a gateway or proxy and received an invalid response from the upstream server.
|
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