In named Entity Recognition (NER), data annotation consists in locating, extracting and tagging proper names entities in text. Entity annotation teaches NLP models how to identify named entities within a text.
CRUD stands for Create, Read, Update and Delete, which are the four basic operations of persistent storage.
Distributed search is a model in which the tasks of data crawling, indexing and query processing are distributed among multiple nodes and networks.
Distributed databases are usually non-relational databases that enable quick access to data over many nodes.
A Domain-Specific language, or DSL, is a language optimized for a specific class of problems. A DSL uses the concepts and rules from the field or domain.
In machine learning, an entity corresponds to any word or series of words that consistently refers to the same thing. Every detected entity is classified into a predetermined category.
Full-text search refers to searching for some text inside extensive text data and returning results that contain some or all the words from the query. In contrast, traditional search would return exact matches.
A database index is a data structure that improves the speed of data retrieval operations on a table. Indexes are used to quickly locate data without having to search every row every time a table is accessed.
Intent recognition or intent classification is the task of taking a written input, and classifying it based on what the user wants to achieve.
Metadata describes data, but it is not the data itself. Author, creation date, modification date and file size are examples of very basic document file metadata.
After building a model, it is necessary to find metrics to measure the goodness of it. The model's performance is generally monitored on new instances that were not a part of the training data.
The goal of model training is to fit the best combination to a machine learning algorithm to optimize it. The purpose is to build the best mathematical representation of the relationship between data features and a target label.
Natural Language Processing, or NLP, is a discipline that focuses on the understanding, manipulation and generation of natural language by machines.
Not Only SQL databases, shortened to NoSQL, store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph. They provide flexible schemas and scale easily with large amounts of data and high user loads.
Sequence of symbols and characters expressing a string or pattern to be searched for within a longer piece of text.
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Analyzers are the algorithms that determine how a string field in a document is transformed into terms in an inverted index. The goal of analyzer is to convert a string into a series of tokens.
Sentiment analysis, or opinion mining, is an NLP technique used to determine whether data is positive, negative or neutral in texts. It can be used, for example, by businesses to gauge brand reputation, customers understanding, and so on.