Benchmarking Language Detection for NLP

Most NLP applications tend to be language-specific and therefore require monolingual data. In order to build an application in your target language, you may need to apply a preprocessing technique that filters out text written in non-target languages. This requires proper identification of the language of each input example. Below I list some tools you can use as Python modules for this preprocessing requirement, and provide a performance benchmark assessing the speed and accuracy of each one.

1. langdetect
2. spaCy language detecto
3. langid
4. FastText

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1) langdetect

langdetect is a re-implementation of Google’s language-detection library from Java to Python. Simply pass your text to the imported detect function and it will output the two-letter ISO 693 code of the language for which the model gave the highest confidence score. (Refer to this page for a full list of 693 codes and their respective languages.) If you use detect_langs instead, it will output a list of the top languages that the model has predicted, along with their probabilities.

from langdetect import DetectorFactory, detect, detect_langs

text = "My lubimy mleko i chleb."
detect(text) ##  'cs'
detect_langs(text)  ## [cs:0.7142840957132709, pl:0.14285810606233737, sk:0.14285779665739756]

A few embellishing points:

1. The library makers recommend that you set the DetectorFactory seed to some number. This is because langdetect’s algorithm is non-deterministic, which means if you try to run it on a text that’s too short or too ambiguous, you might get different results each time you run it. Setting the seed enforces consistent results during development/evaluation.
2. You may also want to surround the detect call in a try/except block with LanguageDetectException, otherwise you will likely get a “No features in text” error, which occurs when there the language of the given input cannot be evaluated as when it contains strings like URLs, numbers, formulas, etc.

from langdetect import DetectorFactory, detect
from langdetect.lang_detect_exception import LangDetectException

DetectorFactory.seed = 0
def is_english(text):
    try:
        if detect(text) != "en":
            return False
    except LangDetectException:
        return False
    return True

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8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

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Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

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Why NLP Suffers From The Issue Of Underrepresented Languages

Natural language processing (NLP) has made several remarkable breakthroughs in recent years by providing implementations for a range of applications including optical character recognition, speech recognition, text simplification, question-answering, machine translation, dialogue systems and much more.

With the help of NLP, systems learn to identify spam emails, suggest medical articles or diagnosis related to a patient’s symptoms, etc. NLP has also been utilised as a critical ingredient in case of crucial decision-making systems such as criminal justice, credit, allocation of public resources, sorting a list of job candidates, to name a few.

However, despite all these critical use cases, NLP is still lagging and faces the problem of underrepresentation. For instance, one of the significant limitations of NLP is the ambiguity of words in languages. The ambiguity and imprecise characteristics of the natural languages make NLP difficult for machines to implement.

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Advantage of C Language Certification Online Training in 2020

C language is a procedural programming language. C language is the general purpose and object oriented programming language. C language is mainly used for developing different types of operating systems and other programming languages. C language is basically run in hardware and operating systems. C language is used many software applications such as internet browser, MYSQL and Microsoft Office.
**
Advantage of doing C Language Training in 2020 are:**

1. Popular Programming language: The main Advantage of doing C language training in 2020 is popular programming language. C programming language is used and applied worldwide. C language is adaptable and flexible in nature. C language is important for different programmers. The basic languages that are used in C language is Java, C++, PHP, Python, Perl, JavaScript, Rust and C- shell.

2. Basic language of all advanced languages: The another main Advantage of doing C language training in 2020 is basic language of all advanced languages. C language is an object oriented language. For learning, other languages, you have to master in C language.

3. Understand the computer theories: The another main Advantage of doing C language training in 2020 is understand the computer theories. The theories such as Computer Networks, Computer Architecture and Operating Systems are based on C programming language.

4. Fast in execution time: The another main Advantage of doing C language training in 2020 is fast in execution time. C language is to requires small run time and fast in execution time. The programs are written in C language are faster than the other programming language.

5. Used by long term: The another main Advantage of doing C language training in 2020 is used by long term. The C language is not learning in the short span of time. It takes time and energy for becoming career in C language. C language is the only language that used by decades of time. C language is that exists for the longest period of time in computer programming history.

6. Rich Function Library: The another main Advantage of doing C language training in 2020 is rich function library. C language has rich function of libraries as compared to other programming languages. The libraries help to build the analytical skills.

7. Great degree of portability: The another main Advantage of doing C language training in 2020 is great degree of portability. C is a portable assemble language. It has a great degree of portability as compilers and interpreters of other programming languages are implemented in C language.
   The demand of C language is high in IT sector and increasing rapidly.

C Language Online Training is for individuals and professionals.
C Language Online Training helps to develop an application, build operating systems, games and applications, work on the accessibility of files and memory and many more.

C Language Online Course is providing the depth knowledge of functional and logical part, develop an application, work on memory management, understanding of line arguments, compiling, running and debugging of C programs.

Is C Language Training Worth Learning for You! and is providing the basic understanding of create C applications, apply the real time programming, write high quality code, computer programming, C functions, variables, datatypes, operators, loops, statements, groups, arrays, strings, etc.

The companies which are using C language are Amazon, Martin, Apple, Samsung, Google, Oracle, Nokia, IBM, Intel, Novell, Microsoft, Facebook, Bloomberg, VM Ware, etc.
C language is used in different domains like banking, IT, Insurance, Education, Gaming, Networking, Firmware, Telecommunication, Graphics, Management, Embedded, Application Development, Driver level Development, Banking, etc.

The job opportunities after completing the C Language Online certificationAre Data Scientists, Back End Developer, Embedded Developer, C Analyst, Software Developer, Junior Programmer, Database Developer, Embedded Engineer, Programming Architect, Game Programmer, Quality Analyst, Senior Programmer, Full Stack Developer, DevOps Specialist, Front End Web Developer, App Developer, Java Software Engineer, Software Developer and many more.

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Benefits Of C Language Over Other Programming Languages

C may be a middle-level programing language developed by Dennis Ritchie during the first 1970s while performing at AT&T Bell Labs within the USA. the target of its development was within the context of the re-design of the UNIX OS to enable it to be used on multiple computers.

Earlier the language B was now used for improving the UNIX. Being an application-oriented language, B allowed a much faster production of code than in programming language. Still, B suffered from drawbacks because it didn’t understand data-types and didn’t provide the utilization of “structures”.

These drawbacks became the drive for Ritchie for the development of a replacement programing language called C. He kept most of the language B’s syntax and added data-types and lots of other required changes. Eventually, C was developed during 1971-73, containing both high-level functionality and therefore the detailed features required to program an OS. Hence, many of the UNIX components including the UNIX kernel itself were eventually rewritten in C.

Benefits of C language

As a middle-level language, C combines the features of both high-level and low-level languages. It is often used for low-level programmings, like scripting for it also supports functions of high-level C programming languages, like scripting for software applications, etc.
C may be a structured programing language that allows a posh program to be broken into simpler programs called functions. It also allows free movement of knowledge across these functions.

Various features of C including direct access to machine level hardware APIs, the presence of C compilers, deterministic resource use, and dynamic memory allocation make C language an optimum choice for scripting applications and drivers of embedded systems.

C language is case-sensitive which suggests lowercase and uppercase letters are treated differently.
C is very portable and is employed for scripting system applications which form a serious a part of Windows, UNIX, and Linux OS.

C may be a general-purpose programing language and may efficiently work on enterprise applications, games, graphics, and applications requiring calculations, etc.
C language features a rich library that provides a variety of built-in functions. It also offers dynamic memory allocation.

C implements algorithms and data structures swiftly, facilitating faster computations in programs. This has enabled the utilization of C in applications requiring higher degrees of calculations like MATLAB and Mathematica.

Riding on these advantages, C became dominant and spread quickly beyond Bell Labs replacing many well-known languages of that point, like ALGOL, B, PL/I, FORTRAN, etc. C language has become available on a really wide selection of platforms, from embedded microcontrollers to supercomputers.

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Natural Language Processing (NLP) Tutorial with Python & NLTK

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=X2vAabgKiuM&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=16
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