Top 10 R Packages For Natural Language Processing (NLP)

R is one of the popular languages for statistical computing among developers and statisticians. According to our latest report, R is the second most-preferred programming language among data scientists and practitioners after Python. The language ruled the preference scale, with a combined figure of 81.9 percent utilisation for statistical modelling among those surveyed.

Read more: https://analyticsindiamag.com/top-10-r-packages-for-natural-language-processing-nlp/

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Top 10 R Packages For Natural Language Processing (NLP)

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.


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.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

CSharp REPL: A Command Line C# REPL with Syntax Highlighting

C# REPL

A cross-platform command line REPL for the rapid experimentation and exploration of C#. It supports intellisense, installing NuGet packages, and referencing local .NET projects and assemblies.

C# REPL screenshot 

(click to view animation)

C# REPL provides the following features:

  • Syntax highlighting via ANSI escape sequences
  • Intellisense with fly-out documentation
  • Nuget package installation
  • Reference local assemblies, solutions, and projects
  • Navigate to source via Source Link
  • IL disassembly (both Debug and Release mode)
  • Fast and flicker-free rendering. A "diff" algorithm is used to only render what's changed.

Installation

C# REPL is a .NET 6 global tool, and runs on Windows 10, Mac OS, and Linux. It can be installed via:

dotnet tool install -g csharprepl

If you're running on Mac OS Catalina (10.15) or later, make sure you follow any additional directions printed to the screen. You may need to update your PATH variable in order to use .NET global tools.

After installation is complete, run csharprepl to begin. C# REPL can be updated via dotnet tool update -g csharprepl.

Usage:

Run csharprepl from the command line to begin an interactive session. The default colorscheme uses the color palette defined by your terminal, but these colors can be changed using a theme.json file provided as a command line argument.

Evaluating Code

Type some C# into the prompt and press Enter to run it. The result, if any, will be printed:

> Console.WriteLine("Hello World")
Hello World

> DateTime.Now.AddDays(8)
[6/7/2021 5:13:00 PM]

To evaluate multiple lines of code, use Shift+Enter to insert a newline:

> var x = 5;
  var y = 8;
  x * y
40

Additionally, if the statement is not a "complete statement" a newline will automatically be inserted when Enter is pressed. For example, in the below code, the first line is not a syntactically complete statement, so when we press enter we'll go down to a new line:

> if (x == 5)
  | // caret position, after we press Enter on Line 1

Finally, pressing Ctrl+Enter will show a "detailed view" of the result. For example, for the DateTime.Now expression below, on the first line we pressed Enter, and on the second line we pressed Ctrl+Enter to view more detailed output:

> DateTime.Now // Pressing Enter shows a reasonable representation
[5/30/2021 5:13:00 PM]

> DateTime.Now // Pressing Ctrl+Enter shows a detailed representation
[5/30/2021 5:13:00 PM] {
  Date: [5/30/2021 12:00:00 AM],
  Day: 30,
  DayOfWeek: Sunday,
  DayOfYear: 150,
  Hour: 17,
  InternalKind: 9223372036854775808,
  InternalTicks: 637579915804530992,
  Kind: Local,
  Millisecond: 453,
  Minute: 13,
  Month: 5,
  Second: 0,
  Ticks: 637579915804530992,
  TimeOfDay: [17:13:00.4530992],
  Year: 2021,
  _dateData: 9860951952659306800
}

A note on semicolons: C# expressions do not require semicolons, but statements do. If a statement is missing a required semicolon, a newline will be added instead of trying to run the syntatically incomplete statement; simply type the semicolon to complete the statement.

> var now = DateTime.Now; // assignment statement, semicolon required

> DateTime.Now.AddDays(8) // expression, we don't need a semicolon
[6/7/2021 5:03:05 PM]

Keyboard Shortcuts

  • Basic Usage
    • Ctrl+C - Cancel current line
    • Ctrl+L - Clear screen
    • Enter - Evaluate the current line if it's a syntactically complete statement; otherwise add a newline
    • Control+Enter - Evaluate the current line, and return a more detailed representation of the result
    • Shift+Enter - Insert a new line (this does not currently work on Linux or Mac OS; Hopefully this will work in .NET 7)
    • Ctrl+Shift+C - Copy current line to clipboard
    • Ctrl+V, Shift+Insert, and Ctrl+Shift+V - Paste text to prompt. Automatically trims leading indent
  • Code Actions
    • F1 - Opens the MSDN documentation for the class/method under the caret (example)
    • F9 - Shows the IL (intermediate language) for the current statement in Debug mode.
    • Ctrl+F9 - Shows the IL for the current statement with Release mode optimizations.
    • F12 - Opens the source code in the browser for the class/method under the caret, if the assembly supports Source Link.
  • Autocompletion
    • Ctrl+Space - Open autocomplete menu. If there's a single option, pressing Ctrl+Space again will select the option
    • Enter, Right Arrow, Tab - Select active autocompletion option
    • Escape - closes autocomplete menu
  • Text Navigation
    • Home and End - Navigate to beginning of a single line and end of a single line, respectively
    • Ctrl+Home and Ctrl+End - Navigate to beginning of line and end across multiple lines in a multiline prompt, respectively
    • Arrows - Navigate characters within text
    • Ctrl+Arrows - Navigate words within text
    • Ctrl+Backspace - Delete previous word
    • Ctrl+Delete - Delete next word

Adding References

Use the #r command to add assembly or nuget references.

  • For assembly references, run #r "AssemblyName" or #r "path/to/assembly.dll"
  • For project references, run #r "path/to/project.csproj". Solution files (.sln) can also be referenced.
  • For nuget references, run #r "nuget: PackageName" to install the latest version of a package, or #r "nuget: PackageName, 13.0.5" to install a specific version (13.0.5 in this case).

Installing nuget packages

To run ASP.NET applications inside the REPL, start the csharprepl application with the --framework parameter, specifying the Microsoft.AspNetCore.App shared framework. Then, use the above #r command to reference the application DLL. See the Command Line Configuration section below for more details.

csharprepl --framework  Microsoft.AspNetCore.App

Command Line Configuration

The C# REPL supports multiple configuration flags to control startup, behavior, and appearance:

csharprepl [OPTIONS] [response-file.rsp] [script-file.csx] [-- <additional-arguments>]

Supported options are:

  • OPTIONS:
    • -r <dll> or --reference <dll>: Reference an assembly, project file, or nuget package. Can be specified multiple times. Uses the same syntax as #r statements inside the REPL. For example, csharprepl -r "nuget:Newtonsoft.Json" "path/to/myproj.csproj"
      • When an assembly or project is referenced, assemblies in the containing directory will be added to the assembly search path. This means that you don't need to manually add references to all of your assembly's dependencies (e.g. other references and nuget packages). Referencing the main entry assembly is enough.
    • -u <namespace> or --using <namespace>: Add a using statement. Can be specified multiple times.
    • -f <framework> or --framework <framework>: Reference a shared framework. The available shared frameworks depends on the local .NET installation, and can be useful when running an ASP.NET application from the REPL. Example frameworks are:
      • Microsoft.NETCore.App (default)
      • Microsoft.AspNetCore.All
      • Microsoft.AspNetCore.App
      • Microsoft.WindowsDesktop.App
    • -t <theme.json> or --theme <theme.json>: Read a theme file for syntax highlighting. This theme file associates C# syntax classifications with colors. The color values can be full RGB, or ANSI color names (defined in your terminal's theme). The NO_COLOR standard is supported.
    • --trace: Produce a trace file in the current directory that logs CSharpRepl internals. Useful for CSharpRepl bug reports.
    • -v or --version: Show version number and exit.
    • -h or --help: Show help and exit.
  • response-file.rsp: A filepath of an .rsp file, containing any of the above command line options.
  • script-file.csx: A filepath of a .csx file, containing lines of C# to evaluate before starting the REPL. Arguments to this script can be passed as <additional-arguments>, after a double hyphen (--), and will be available in a global args variable.

If you have dotnet-suggest enabled, all options can be tab-completed, including values provided to --framework and .NET namespaces provided to --using.

Integrating with other software

C# REPL is a standalone software application, but it can be useful to integrate it with other developer tools:

Windows Terminal

To add C# REPL as a menu entry in Windows Terminal, add the following profile to Windows Terminal's settings.json configuration file (under the JSON property profiles.list):

{
    "name": "C# REPL",
    "commandline": "csharprepl"
},

To get the exact colors shown in the screenshots in this README, install the Windows Terminal Dracula theme.

Visual Studio Code

To use the C# REPL with Visual Studio Code, simply run the csharprepl command in the Visual Studio Code terminal. To send commands to the REPL, use the built-in Terminal: Run Selected Text In Active Terminal command from the Command Palette (workbench.action.terminal.runSelectedText).

Visual Studio Code screenshot

Windows OS

To add the C# REPL to the Windows Start Menu for quick access, you can run the following PowerShell command, which will start C# REPL in Windows Terminal:

$shell = New-Object -ComObject WScript.Shell
$shortcut = $shell.CreateShortcut("$env:appdata\Microsoft\Windows\Start Menu\Programs\csharprepl.lnk")
$shortcut.TargetPath = "wt.exe"
$shortcut.Arguments = "-w 0 nt csharprepl.exe"
$shortcut.Save()

You may also wish to add a shorter alias for C# REPL, which can be done by creating a .cmd file somewhere on your path. For example, put the following contents in C:\Users\username\.dotnet\tools\csr.cmd:

wt -w 0 nt csharprepl

This will allow you to launch C# REPL by running csr from anywhere that accepts Windows commands, like the Window Run dialog.

Comparison with other REPLs

This project is far from being the first REPL for C#. Here are some other projects; if this project doesn't suit you, another one might!

Visual Studio's C# Interactive pane is full-featured (it has syntax highlighting and intellisense) and is part of Visual Studio. This deep integration with Visual Studio is both a benefit from a workflow perspective, and a drawback as it's not cross-platform. As far as I know, the C# Interactive pane does not support NuGet packages or navigating to documentation/source code. Subjectively, it does not follow typical command line keybindings, so can feel a bit foreign.

csi.exe ships with C# and is a command line REPL. It's great because it's a cross platform REPL that comes out of the box, but it doesn't support syntax highlighting or autocompletion.

dotnet script allows you to run C# scripts from the command line. It has a REPL built-in, but the predominant focus seems to be as a script runner. It's a great tool, though, and has a strong community following.

dotnet interactive is a tool from Microsoft that creates a Jupyter notebook for C#, runnable through Visual Studio Code. It also provides a general framework useful for running REPLs.

Download Details:
Author: waf
Source Code: https://github.com/waf/CSharpRepl
License: MPL-2.0 License

#dotnet  #aspdotnet  #csharp 

Sival Alethea

Sival Alethea

1624381200

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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#natural language processing #nlp #python #python & nltk #nltk #natural language processing (nlp) tutorial with python & nltk

Paula  Hall

Paula Hall

1623392820

Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

#analytics #nlp #machine-learning #data-science #structured natural language processing with pandas and spacy #natural language processing

Kolby  Wyman

Kolby Wyman

1596726420

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.

#developers corner #issues in nlp #natural language processing #nlp ai #nlp papers #nlp research