A concept that aims to understand the mechanism behind programming computers to process and analyze large amounts of natural language data, tends to yield a massive field of research. Exploring such a diverse area tends to not only be a challenging situation for those navigating through it but also sometimes it’s difficult to even know how to take the first step into the search for dataSo, In hopes of easing you into the world of natural language processing. We are presenting a list of online Datasets for Natural Language Processing-NLP that cover a wide arrange of topics. 

In the world of natural language processing, the aim is to create a learning environment for computers that is not only optimized but also provides a wide range of sources through which the computers can learn. This process is integral to the idea of learning, un-learning, and re-learning – an ideology that aims to drive change within the world of computing.

NLP for Business

There’s no surprise that in today’s world more and more businesses have begun sifting through sources in an attempt to better understand their markets and consumers to establish relationships that are better suited to individual needs. To grow it has now become imperative for businesses to incorporate NLP’s into their systems and invigorate a sense of growth and change within their organizations.
 
However, with so many businesses working with the same motive and looking for practically the same sources, there obviously is a lot of concern with regards to where one can really start this entire process. The options are endless but there is no certainty as to what option suits what particular needs.

Through this list of data sets for NLP, we hope to provide you with resources that will ease your transition into the globalized world of natural language processing.

Discover Applications of NLP- Natural Language Processing

NLP Datasets

Let’s see a list of NLP Datasets that can help you decipher the plethora of information available online.

Datasets for Sentiment Analysis

Refer to the use of natural language processing to identify, extract, quantify & study affective states & subjective information. All of which points to the need for a large, expertise dataset. So, what are some of the datasets that can help you do that?
 

Stanford Sentiment Treebank:

Home to over 10,000 clips from Rotten Tomatoes, Stanford’s dataset is to help identify sentiment in longer phrases,i.e., get the system accustomed to detailed data.

Multidomain Sentiment Analysis Dataset:

Despite being an older dataset. It offers many product reviews taken from Amazon that help with the provision of diverse data.

IMDB Reviews:

Like Stanford’s treebank, this dataset consists of over 25,000 movie reviews that are useful for a rather binary classification use.

 

Text Datasets

Not only are these datasets easier to access, but they are also easier to input and use for natural language processing tasks about the inclusion of chatbots and voice recognition.
 

The Blog Authorship Corpus:

With over 681,000 posts by over 19,000 independent bloggers. This dataset is home to over 140 million words; which on its own poses it as a valuable dataset.

UCI’s Spambase:

Creation of the team at Hewlett-Packard. This dataset consists of a wide array of spam email that can be used to create spam filters.

The WikiQA Corpus:

This is one of the most accessible collections of questions/answers. The dataset is here for research purposes in the domain of question answering but has now become a public depository for anyone concerned with natural language processing.

Wordnet:

A product of researchers at Princeton University. Wordnet offers a large database consisting of synonyms in the English language with each describing a unique concept.
 

Yelp Reviews:

Available for the public, this dataset contains millions of reviews received by Yelp over the years.

Legal case reports dataset:

The law has always been a little too wordy. This is something that works in the favor of NLP. With text summaries and wrap-ups of over 4000 legal cases, this is a great avenue for training machines in automatic text summarization while also learning a wide array of textual knowledge.

20 Newsgroups:

The newspaper industry has always been our first resource to garner information about the world and whatever’s happening in it. If it’s worked for us, why won’t it work for machines? With over 20,000 documents from 20 different newsgroups, this resource covers a variety of topics with some closely related for reference purposes.

Audio Datasets

When training natural language processing applications to act as virtual assistants, navigation, or any other sound-activated systems. One finds audio speech datasets as most useful.
 

LibriSpeech:

With over 1,000 hours of English speech available from a range of audiobooks dictated by many speakers. This dataset stands out as one of the most diverse resources available.
 

Free Spoken Digit Dataset:

Since numbers are key, this collection offers 1,500 recordings of digits spoken in the English language

General Datasets

In a world where knowledge knows no bounds. There’s a great chance that you might be looking for something that doesn’t fall under any of the above-mentioned categories. So, some general datasets that can help you with any natural language processing task include:
 

Wikipedia Links Data:

If there’s any dataset that has the potential to hold universal information. It’s the dataset by Google consisting of webpages leading to Wikipedia.
 

Jeopardy:

This is from the famous TV show that has catered to millions of people throughout the globe. With a dataset spanning over 200,000 questions taken from the show, it includes category and value designations as well coupled with other descriptors dealing with question and answer fields alongside rounds. With this particular data source, machines face a wide array of datasets and information belonging to multiple sources/avenues.
 

SMS Spam Collection in English:

Who says spam texts can’t be useful? With over 5,000 spam messages. This dataset offers information like none other.

Recommender systems datasets:

Home to datasets from a variety of sources, this particular resource will welcome you to the world of fitness, gaming, and social media. With data sourced from fitness tracking, online gaming, and interactions, this source provides you with labels concerning star ratings, time stamps, social networks, and images.

Project Gutenberg:

Book texts are probably the only ones that can offer you a wide range of datasets that will fit in with any requirements that you may have. Here, you will have access to texts in a wide array of languages, and from time periods that span multiple years. What’s best about is that it serves itself as a public domain which means that the available data is almost always increased/reinvented as more and more individuals get on board.

Summing Up…

The above information is a suite of standard datasets that are in use for natural language processing tasks when getting started with deep learning.
Therefore, It is preferable to use small datasets and do not take too long to fit models. Also, it is beneficial to use standard datasets that are understandable. You can compare the outcomes to have a clear picture of the progress that you are making.
 
So, with this provided information you can find your nlp data sets and go on!