The de-facto standard in many natural language processing (NLP) tasks nowadays is to use a transformer. Text generation? Transformer. Question-and-answering? Transformer. Language classification? Transformer!
However, one of the problems with many of these models (a problem that is not just restricted to transformer models) is that we cannot process long pieces of text.
Almost every article I write on Medium contains 1000+ words, which, when tokenized for a transformer model like BERT, will produce 1000+ tokens. BERT (and many other transformer models) will consume 512 tokens max — truncating anything beyond this length.
Although I think you may struggle to find value in processing my Medium articles, the same applies to many useful data sources — like news articles or Reddit posts.
We will take a look at how we can work around this limitation. In this article, we will find the sentiment for long posts from the /r/investing subreddit. This article will cover:
#python #machine-learning #data-science #pytorch #tensorflow #deep-learning
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:
#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm
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.
Let’s start laravel full-text search implementation in laravel 7, 6 versions:
#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
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 …
The components of a text analyzer can broadly be classified into 2 categories:
Couchbase’s engine further categorizes filters into:
Before we dive into the function of each of these components, here’s an overview of a text analyzer …
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.
Take this sample text for an example: “_this is my email ID: email@example.com”
A couple of configurable tokenizers…
#json #couchbase #search #go #text analysis #full-text search #bleve #full-text #full-text-indexing
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