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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.
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.
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.
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 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.
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 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.
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 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.
Yonder Labs is a data science company with a special expertise in Natural Language Processing, Machine Learning, and Multimedia Analysis. Yonder is currently releasing new API for extracting semantic information both from single text documents, such as sentiment analysis, entity extraction, semantic tagging, etc. and from collections of texts, allowing for services such as text comparison, clustering, and data mining on text collections.
MonkeyLearn is a Text Analysis platform with Machine Learning to automate business workflows and save hours of manual data processing. They provide pre-built NLP APIs adapted to use cases such as entity extraction, sentiment analysis, text classification, etc. With MonkeyLearn you can also train custom machine learning models to get topic, sentiment, intent, keywords and more.
Intellexer is a linguistic platform which incorporates powerful linguistic tools for analyzing text in natural language. Intellexer Language Recognizer combines statistic and linguistic technologies in order to obtain the highest recognition results. Our language detection algorithm is based on strong mathematical model of vector spacing algorithm. It creates multidimensional space of vectors scanning document contests and uses N-grams notion for calculating their frequencies.
Cortical.io provides natural language understanding (NLU) solutions that enable large enterprises to automate the extraction, monitoring, and analysis of key information from any kind of text data. Cortical.io offers AI-based natural language understanding solutions built on technology inspired by Neuroscience.
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pre-trained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
You can use Language Detection in numerous fields, here are some examples of common use cases:
When you need a Language Detection engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Language Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Language Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production.
Here is the code in Python (GitHub repo) that allows to test Eden AI for language detection:
There are numerous Language Detection engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Language Detection technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
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