Top 10 Language Detection APIs

Updated: Dec 17, 2021



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Intro:

In this article, we are going to see how we can easily integrate a Language Detection engine in your project and how to choose and access the right engine according to your data.


Definition:


Language detection is the task of automatically detecting the language(s) present in a document based on the content of the document. Using a language detection engine, you can obtain the most likely language for a piece of input text, or a set of possible language candidates with their associated probabilities.


History:


Language detection predates computational methods – the earliest interest in the area was motivated by the needs of translators, and simple manual methods were developed to quickly identify documents in specific languages. The earliest known work to describe a functional Language detection program for text is by Mustonen in 1965, who used multiple discriminant

analysis to teach a computer how to distinguish between English, Swedish and Finnish.


In the early 1970s, Nakamura considered the problem of automatic Language detection. His language identifier was able to distinguish between 25 languages written with the Latin alphabet. As features, the method used the occurrence rates of characters and words in each language.

The highest-cited early work on automatic language detection is Cavnar and Trenkle in1994. Cavnar and Trenkle method builds up per-document and per-language profiles, and classifies a document according to which language profile it is most similar to, using a rank-order similarity metric.


Top 10 Language Detection APIs:

Microsoft Azure - Available on Eden AI

The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.


Available on Eden AI


TextRazor

TextRazor offers a complete cloud or self-hosted text analysis infrastructure. They combine state-of-the-art natural language processing techniques with a comprehensive knowledgebase of real-life facts to help rapidly extract the value from your documents, tweets or web pages. They provide features such as entity extraction, disambiguation and linking, keyphrase extraction, automatic topic tagging and classification.


Google Cloud - Available on Eden AI

The Cloud Natural Language API provides natural language understanding technologies to developers, including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. This API is part of the larger Cloud Machine Learning API family. Each API call also detects and returns the language, if a language is not specified by the caller in the initial request.


Available on Eden AI


Meaning Cloud

Meaning Cloud provides text analytics products to extract the most accurate insights from any multimedia content in many languages. And they do it SaaS and On-prem. They work for different industries (pharma, finance, media, retail, hospitality, telco, etc.) developing personalized and industry-oriented solutions. Meaning Cloud sentiment analysis API performs a detailed, multilingual sentiment analysis on information from different sources.


AWS - Available on Eden AI

Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. Amazon Comprehend processes any text file in UTF-8 format, and semi-structured documents, like PDF and Word documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.


Available on Eden AI


Dandelion

Dandelion API is a set of semantic APIs to extract meaning and insights from texts in several languages (Italian, English, French, German and Portuguese). It’s optimized to perform text mining and text analytics for short texts, such as tweets and other social media. Dandelion API extracts entities (such as persons, places and events), categorizes and classifies documents in user-defined categories, augments the text with tags and links to external knowledge graphs and more.