Best Natural Language Processing (NLP) Datasets, Databases & APIs
Tailor-made Data Mining & Processing Solutions by PREDIK Data-Driven
Fully labelled Datasets of Arabic Language for Machine Learning - Text & Audio NLP Data - Kieli
TAUS Language Translation Data | Parallel translation for E- Commerce, various language pairs
Agents Republic | Custom Multilingual Conversational AI Training Data via Audio/Voice (50+ languages)
Kieli NLP Data - Fully-labelled dataset of Arabic language for Machine Learning & AI platforms
TAUS Language Translation Data | Parallel translation for Colloquial English into various languages for
Automaton AI Data labeling services
Linguistic Services by EPIC Translations: Linguistic Annotation Data for AI & ML
Cyabra's Analytics Platform for Online Conversations - Global Online Sentiment Data
Kieli NLP Data - Fully-labelled Audio & Text Dataset for Machine Learning & AI platforms
The Ultimate Guide to Natural Language Processing (NLP) Data 2022
What is Natural Language Processing (NLP) Data?
Artificial intelligence continues to gain more traction in contemporary technological advancement. As this field spreads into various sectors, it has found applications in machine learning techniques through aspects such as natural language processing where computer systems are being programmed to comprehend, interpret and manipulate human languages. This development has made a lots of well-publicised strides, as seen in Google’s Android Assistant, Apple’s Siri, and Amazon’s Alexa voice assistant programs that understand human language and are used to process data.
How is Natural Language Processing (NLP) Data collected?
NLP data is collected through rule-based models which are considered the oldest means that were hand-written and hand-coded during the earlier stages of NLP development. On the other hand, the more modern statistical-based models calls on machine learning techniques to infer and interpret language learning rules through the analysis of real-world instances of large datasets. Through machine learning algorithms, NLP data is collected from the programs that are designed to identify and learn recurring patterns to focus autonomously on certain areas of the input text.
What are the typical attributes of Natural Language Processing (NLP) Data?
Natural language processing data is made up of machine learning algorithms currently in use, statistical models for computer mapping of information, and rule-based modeling approaches. These attributes combine efforts to help computer systems process human language data. Furthermore, some of the aspects that make up NLP data include text-to-speech or speech-to-text conversions, machine translation from one language to another (e.g. Google Translate), categorizing, indexing, and summarizing written documents, and the ability of computer systems to identify moods and opinions within the text and voice-based data.
What is Natural Language Processing (NLP) Data used for?
NLP data is used by computer systems to help in the breakdown of large categories of human language data into smaller, shorter, concise, and more logical components with the sole purpose of comprehending the semantic and syntactic purpose of spoken and written human language. Advancement in machine learning means that with more NLP data, it is now possible for computer systems to analyze data at a much faster rate helping bridge the gap in a large volume of data that is accumulated due to slow processing. Machines that are designed with machine learning algorithms can analyze and comprehend more language data than humans because they have the ability to process more language patterns, thanks to NLP data.
How can a user assess the quality of Natural Language Processing (NLP) Data?
When determining the quality of NLP data, users should apply the data quality index (DQI) techniques to determine that the data entries are correct, there are no duplicates of specific data units, and that the referential integrity of the data is correct for a natural language processing database. A user can apply the core principles of DQI to filter out any traces of bias in the dataset while also assessing the validity of counterfactual data.
Where can I buy Natural Language Processing (NLP) Data?
Data providers and vendors listed on Datarade sell Natural Language Processing (NLP) Data products and samples. Popular Natural Language Processing (NLP) Data products and datasets available on our platform are Tailor-made Data Mining & Processing Solutions by PREDIK Data-Driven by Predik Data-driven, Fully labelled Datasets of Arabic Language for Machine Learning - Text & Audio NLP Data - Kieli by Kieli, and TAUS Language Translation Data | Parallel translation for E- Commerce, various language pairs by TAUS.
How can I get Natural Language Processing (NLP) Data?
You can get Natural Language Processing (NLP) Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Natural Language Processing (NLP) Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Natural Language Processing (NLP) Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Natural Language Processing (NLP) Data?
Natural Language Processing (NLP) Data is similar to Annotated Imagery Data, Machine Learning (ML) Data, Deep Learning (DL) Data, and Synthetic Data. These data categories are commonly used for Machine Learning (ML) and Deep Learning.
What are the most common use cases for Natural Language Processing (NLP) Data?
The top use cases for Natural Language Processing (NLP) Data are Machine Learning (ML), Deep Learning, and Data Science.