Toni  Schmidt

Toni Schmidt

1614434880

A Simple Text Summarizer written in Rust

Motivation

I read a lot of online news sites to help me catch up with the latest stories. From time to time, I found that some news stories from different sources are simply rephrasing each other. This happens sometimes with technical articles too, but I digress.

This love of reading news stories lead me into the idea of text summarization. Wouldn’t it be great if I can summarize across multiple news sources and let me gather all the information?

I have another goal in mind. I love computer languages. Rust has been on my radar for a long time but I have never come up with a good excuse to learn to use it. I figured that a data process program like text summarization could be a good fit to learn about the gory details of memory ownership.

Text Summarization in Short

There are basically two techniques when it comes to text summarization: Abstractive and Extractive.

Abstraction-based summarization takes words from the original article based on semantic meaning. It can sometimes pick words from another source if the words fit the meaning. The idea is not unlike how a human would have summarize a piece of text. As you can imagine, this is not an easy problem and would almost require some form of machine understanding.

Extraction-based summarization takes a different approach. Instead of trying to understand the underlying text. It uses some mathematical formulas to rank each sentence from the article and output only sentences that are above certain score. This way, the meaning of the original text is mostly preserved without coding the machine to understand.

In this article, we will use an extraction based summarization technique. It’s perfect for individual developer, like me (or you), to experiment with and to appreciate the problem.

#rust

What is GEEK

Buddha Community

A Simple Text Summarizer written in Rust
Daron  Moore

Daron Moore

1598404620

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

  • NLP Functionalities of Pattern
  • Data Mining Using Pattern

#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

I am Developer

1597475640

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

  1. Step 1: Install Laravel New App
  2. Step 2: Configuration DB .evn file
  3. Step 3: Run Migration
  4. Step 4: Install Full Text Search Package
  5. Step 5: Add Fake Records in DB
  6. Step 6: Add Routes,
  7. Step 7: Create Controller
  8. Step 8: Create Blade View
  9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel

Toni  Schmidt

Toni Schmidt

1614434880

A Simple Text Summarizer written in Rust

Motivation

I read a lot of online news sites to help me catch up with the latest stories. From time to time, I found that some news stories from different sources are simply rephrasing each other. This happens sometimes with technical articles too, but I digress.

This love of reading news stories lead me into the idea of text summarization. Wouldn’t it be great if I can summarize across multiple news sources and let me gather all the information?

I have another goal in mind. I love computer languages. Rust has been on my radar for a long time but I have never come up with a good excuse to learn to use it. I figured that a data process program like text summarization could be a good fit to learn about the gory details of memory ownership.

Text Summarization in Short

There are basically two techniques when it comes to text summarization: Abstractive and Extractive.

Abstraction-based summarization takes words from the original article based on semantic meaning. It can sometimes pick words from another source if the words fit the meaning. The idea is not unlike how a human would have summarize a piece of text. As you can imagine, this is not an easy problem and would almost require some form of machine understanding.

Extraction-based summarization takes a different approach. Instead of trying to understand the underlying text. It uses some mathematical formulas to rank each sentence from the article and output only sentences that are above certain score. This way, the meaning of the original text is mostly preserved without coding the machine to understand.

In this article, we will use an extraction based summarization technique. It’s perfect for individual developer, like me (or you), to experiment with and to appreciate the problem.

#rust

Rust Lang Course For Beginner In 2021: Guessing Game

 What we learn in this chapter:
- Rust number types and their default
- First exposure to #Rust modules and the std::io module to read input from the terminal
- Rust Variable Shadowing
- Rust Loop keyword
- Rust if/else
- First exposure to #Rust match keyword

=== Content:
00:00 - Intro & Setup
02:11 - The Plan
03:04 - Variable Secret
04:03 - Number Types
05:45 - Mutability recap
06:22 - Ask the user
07:45 - First intro to module std::io
08:29 - Rust naming conventions
09:22 - Read user input io:stdin().read_line(&mut guess)
12:46 - Break & Understand
14:20 - Parse string to number
17:10 - Variable Shadowing
18:46 - If / Else - You Win, You Loose
19:28 - Loop
20:38 - Match
23:19 - Random with rand
26:35 - Run it all
27:09 - Conclusion and next episode

#rust 

Hollie  Ratke

Hollie Ratke

1597989600

Text Analysis Within a Full-Text Search Engine

Full-Text Search refers to techniques for searching text content within a document or a collection of documents that hold textual content. A Full-Text search engine examines all the textual content within documents as it tries to match a single search term or several terms, text analysis being a pivotal component.

You’ve probably heard of the most well-known Full-Text Search engine: Lucene with Elasticsearch built on top of it. Couchbase’s Full-Text Search (FTS) Engine is powered by Bleve, and this article will showcase the various ways to analyze text within this engine.

Bleve is an open-sourced text indexing and search library implemented in Go, developed in-house at Couchbase.

Couchbase’s FTS engine supports indexes that subscribe to data residing within a Couchbase Server and indexes data that it ingests from the server. It’s a distributed system – meaning it can partition data across multiple nodes in a cluster and searches involve scattering the request and gathering responses from across all nodes within the cluster before responding to the application.

The FTS engine distributes documents ingested for an index across a configurable number of partitions and these partitions could reside across multiple nodes within a cluster. Each partition follows the same set of rules that the FTS index is configured with – to analyze and index text into the full-text search database.

The text analysis component of a Full-Text search engine is responsible for breaking down the raw text into a list of words – which we’ll refer to as tokens. These tokens are more suitable for indexing in the database and searching.

Couchbase’s FTS Engine handles text indexing for JSON documents. It builds an index for the content that is analyzed and stores into the database – the index along with all the relevant metadata needed to link the tokens generated to the original documents within which they reside.

An Inverted index is the data structure chosen to index the tokens generated from text, to make search queries faster. This index links every token generated to documents that contain the token.

For example, take the following documents …

The inverted index for the tokens generated from the 2 documents above would resemble this…

Here’s a diagram highlighting the components of the full-text search engine …

A Text Analyzer

The components of a text analyzer can broadly be classified into 2 categories:

  • Tokenizer
  • Filters

Couchbase’s engine further categorizes filters into:

  • Character filters
  • Token filters

Before we dive into the function of each of these components, here’s an overview of a text analyzer …

Tokenizer

A tokenizer is the first component to which the documents are subjected to. As the name suggests, it breaks the raw text into a list of tokens. This conversion will depend on a rule-set defined for the tokenizer.

Stock tokenizers…

Take this sample text for an example: “_this is my email ID: _abhi123@cb.com

A couple of configurable tokenizers…

  • Exception … This tokenizer allows the user to enter exception patterns (regular expressions) over the stock tokenizers.
  • Regexp … This tokenizer extracts text that matches the pattern (a regular expression) as tokens.

For example:

#json #couchbase #search #go #text analysis #full-text search #bleve #full-text #full-text-indexing