Text Tagging API

Text Tagging API

The Text Tagging API provides an easy and efficient way to analyze text by identifying the parts of speech, grouping them into meaningful phrases, and recognizing named entities. With this API, developers can automate tasks such as content categorization, sentiment analysis, and entity recognition, improving the accuracy and efficiency of text-processing workflows.

About the API: 

The Text Tagging API is a powerful tool for developers who need to analyze and process text data. This API provides several useful features such as part-of-speech tagging, phrase chunking, and named entity recognition.

Part-of-speech tagging is the process of identifying the parts of speech in a sentence, such as nouns, verbs, adjectives, and adverbs. This information can be used to identify the grammatical structure of a sentence, which is useful for tasks such as text classification, sentiment analysis, and information retrieval. By identifying the parts of speech, developers can more easily extract meaningful information from large text datasets.

Phrase chunking is the process of grouping words together into meaningful phrases. This can be used to identify phrases such as noun phrases, verb phrases, and prepositional phrases. By identifying these phrases, developers can more easily extract meaningful information from text datasets. For example, in a news article about a political event, identifying noun phrases such as "president", "election", and "opposition party" can help in identifying the main topics of the article.

Named entity recognition (NER) is the process of identifying and categorizing named entities in text. This includes identifying names of people, organizations, locations, and other entities. By identifying these named entities, developers can extract information about the entities mentioned in text, which can be useful for tasks such as information retrieval, sentiment analysis, and content categorization. For example, in a news article about a company acquisition, identifying the names of the companies involved and the location of the acquisition can help in categorizing the article as a business news story.

The Text Tagging API is easy to use, with simple REST API calls that return JSON formatted results. Developers can use this API to automate tasks such as content categorization, sentiment analysis, and entity recognition. This can improve the accuracy and efficiency of text processing workflows, making it easier for developers to work with large text datasets. The API can also be used to build intelligent applications such as chatbots, virtual assistants, and search engines that can understand natural language text input.

Overall, the Text Tagging API is a powerful tool for developers who need to analyze and process text data. With its part-of-speech tagging, phrase chunking, and named entity recognition features, this API can help developers extract meaningful information from large text datasets, improving the accuracy and efficiency of text processing workflows.

 

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

Pass the text that you want to analyze and receive its tagging and part of speech analysis.  

 

What are the most common uses cases of this API?

  1. Sentiment analysis: The Text Tagging API can be used to identify the parts of speech, phrases, and named entities in customer reviews or social media posts, which can be used to perform sentiment analysis. This can help businesses understand customer sentiment and feedback about their products or services, and improve their offerings accordingly.

  2. Content categorization: The Text Tagging API can be used to analyze the text of news articles or social media posts, and categorize them into topics such as sports, politics, or entertainment. This can help news organizations or social media platforms better organize and present content to users, improving user engagement and satisfaction.

  3. Virtual assistants and chatbots: The Text Tagging API can be used to help virtual assistants or chatbots better understand and respond to natural language input from users. By identifying the parts of speech and named entities in user input, these intelligent systems can provide more accurate and helpful responses to user queries.

  4. Search engines: The Text Tagging API can be used to improve the accuracy of search results in search engines. By identifying the parts of speech and named entities in user search queries, search engines can better understand the intent behind the query and provide more relevant search results.

  5. Data mining: The Text Tagging API can be used to extract valuable information from large text datasets such as academic papers, legal documents, or social media posts. By identifying the parts of speech, phrases, and named entities in these texts, researchers or analysts can uncover new insights or trends, and make data-driven decisions based on their findings.

 

Are there any limitations to your plans?

Besides the number of API calls, there is no other limitation

API Documentation

Endpoints


Part-of-speech tagging, phrase chunking, and named entity recognition of text.

Available Languages:

english. 

spanish.

dutch. 

portuguese. 



                                                                            
POST https://zylalabs.com/api/1818/text+tagging+api/1479/text+tagging
                                                                            
                                                                        

Text Tagging - Endpoint Features
Object Description
text [Required] Text to tag and chunk, must be no more than 2000 characters.
language [Required] The default language is english, which along with dutch, portuguese, and spanish supports phrase chunking and named entity recognition. T
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"text": "The/DT word/NN logorrhoea/NN is/VBZ often/RB used/VBN pejoratively/RB to/TO describe/VB prose/NN that/WDT is/VBZ highly/RB abstract/JJ and/CC contains/VBZ little/JJ concrete/JJ language/NN ./.. Since/IN abstract/NN writing/VBG is/VBZ hard/JJ to/TO visualize/VB ,/, it/PRP often/RB seems/VBZ as/IN though/IN it/PRP makes/VBZ no/DT sense/NN and/CC all/DT the/DT words/NNS are/VBP excessive/JJ ./.. Writers/NNS in/IN academic/JJ fields/NNS that/WDT concern/NN themselves/VBZ mostly/RB with/IN the/DT abstract/NN ,/, such/JJ as/IN philosophy/NN and/CC especially/RB postmodernism/NN ,/, often/RB fail/VBP to/TO include/VB extensive/JJ concrete/JJ examples/NNS of/IN their/PRP$ ideas/NNS ,/, and/CC so/RB a/DT superficial/JJ examination/NN of/IN their/PRP$ work/NN might/MD lead/VB one/CD to/TO believe/VB that/IN it/PRP is/VBZ all/DT nonsense/NN ./."}
                                                                                                                                                                                                                    
                                                                                                    

Text Tagging - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/1818/text+tagging+api/1479/text+tagging?text=The word logorrhoea is often used pejoratively  to describe prose that is highly abstract and  contains little concrete language. Since abstract  writing is hard to visualize, it often seems as though  it makes no sense and all the words are excessive.  Writers in academic fields that concern themselves mostly  with the abstract, such as philosophy and especially  postmodernism, often fail to include extensive concrete  examples of their ideas, and so a superficial examination  of their work might lead one to believe that it is all nonsense.&language=english' --header 'Authorization: Bearer YOUR_API_KEY' 

    

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 Text Tagging API REST 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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 Response Time
441ms

Category:

NLP

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