Part-Of-Speech Tagging API

API ID 821

This API will help you tag different parts of any speech provided. Tags the provided text with parts of speech (such as nouns, verbs, adjectives, adverbs, etc.)

100% uptime 176 ms avg response

API Documentation

Endpoints

Request

Tags the provided text with parts of speech (such as nouns, verbs, adjectives, adverbs, etc.).

Part-of-Speech Labels:
CC - Coordinating conjunction
CD - Cardinal number
DT - Determiner
EX - Existential there
FW - Foreign word
HYPH - Hyphen
IN - Preposition or subordinating conjunction
JJ - Adjective
JJR - Adjective, comparative
JJS - Adjective, superlative
LS - List item marker
MD - Modal
NFP - Superfluous punctuation
NN - Noun, singular or mass
NNP - Proper noun, singular
NNPS - Proper noun, plural
NNS - Noun, plural
PDT - Predeterminer
POS - Possessive ending
PRP - Personal pronoun
PRP$ - Possessive pronoun
RB - Adverb
RBR - Adverb, comparative
RBS - Adverb, superlative
RP - Particle
SYM - Symbol
TO - to
UH - Interjection
VB - Verb, base form
VBD - Verb, past tense
VBG - Verb, gerund or present participle
VBN - Verb, past participle
VBP - Verb, non-3rd person singular present
VBZ - Verb, 3rd person singular present
WDT - Wh-determiner
WP - Wh-pronoun
WP$ - Possessive wh-pronoun
WRB - Wh-adverb
XX - Unknown
ADD - Email
AFX - Affix

Endpoint ID: 580
POST https://zylalabs.com/api/821/part-of-speech+tagging+api/580/pos+tagging
INPUT PARAMETERS

POS Tagging — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"badRequest": false, "creditsUsed": 1, "errors": [], "forbidden": false, "invalidSession": false, "noSessionFound": false, "notAuthorized": false, "sentences": [{"tokens": [{"labels": [{"label": "EX", "score": 0.9999990463256836}], "text": "There"}, {"labels": [{"label": "VBZ", "score": 0.9999969005584717}], "text": "is"}, {"labels": [{"label": "DT", "score": 0.9999946355819702}], "text": "a"}, {"labels": [{"label": "NN", "score": 0.999990701675415}], "text": "house"}, {"labels": [{"label": "IN", "score": 0.9999613761901855}], "text": "by"}, {"labels": [{"label": "DT", "score": 0.9999933242797852}], "text": "the"}, {"labels": [{"label": "NN", "score": 0.9998831748962402}], "text": "lake"}, {"labels": [{"label": "WDT", "score": 0.993063747882843}], "text": "that"}, {"labels": [{"label": "PRP", "score": 1.0}], "text": "we"}, {"labels": [{"label": "MD", "score": 0.9999997615814209}], "text": "could"}, {"labels": [{"label": "VB", "score": 0.9998855590820312}], "text": "use"}, {"labels": [{"label": "DT", "score": 0.999998927116394}], "text": "this"}, {"labels": [{"label": "NN", "score": 0.9999990463256836}], "text": "summer"}, {"labels": [{"label": ".", "score": 0.9999368190765381}], "text": "."}]}], "statusCode": 200, "tooManyRequests": false}
POS Tagging — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/821/part-of-speech+tagging+api/580/pos+tagging' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
    "text": "There is a house by the lake that we could use this summer."
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Part-Of-Speech Tagging API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

About the API:

In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech,[1] based on both its definition and its context. A simplified form of this is commonly taught to school-age children, in the identification of words as nounsverbsadjectivesadverbs, etc.

 

What this API receives and what your API provides (input/output)?

Pass any text to this API as a JSON, and receive all the possible tags for the different parts of the speech.  

 

What are the most common uses cases of this API?

 

Use this API to retrieve semantic information from given texts. 

Great API for Machine Learning training. Use the data to train your models so they can easily recognize different parts of the speech as well. 

 

Are there any limitations to your plans?

Besides API call limitations per month, there are no other limitations.

Part-Of-Speech Tagging API FAQs

The POS Tagging API returns a JSON object containing tagged parts of speech for each token in the provided text. The response includes fields such as "badRequest," "creditsUsed," "errors," and "sentences," where each sentence contains an array of tokens with their respective labels and confidence scores.

Key fields in the response include "badRequest" (indicates request validity), "creditsUsed" (number of credits consumed), "errors" (any processing errors), and "sentences" (array of sentences with tokens and their associated tags).

The response data is structured as a JSON object. It contains a top-level object with fields for request status and an array of "sentences," each containing "tokens." Each token includes "text" and "labels," where "labels" provide the part of speech and confidence score.

The API provides information on parts of speech for each word in the input text, including labels for nouns, verbs, adjectives, adverbs, and more. Each label is accompanied by a confidence score indicating the accuracy of the tagging.

Users can customize their requests by providing different text inputs in the JSON format. The API processes any valid text, allowing for flexibility in the type and length of text analyzed for part-of-speech tagging.

Typical use cases include natural language processing tasks, such as semantic analysis, text classification, and training machine learning models to recognize parts of speech. It can also be used in educational tools for teaching grammar.

Data accuracy is maintained through advanced algorithms that analyze context and definitions of words. The API uses machine learning techniques to improve tagging precision based on extensive linguistic datasets.

Users can expect a consistent structure in the response, with each token labeled according to its part of speech. Common patterns include multiple tokens per sentence, each with a label and score, facilitating easy parsing and analysis.

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