Model evaluation

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.

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Model training

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.

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Regular expression

Sequence of symbols and characters expressing a string or pattern to be searched for within a longer piece of text.

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Intention

Intent recognition or intent classification is the task of taking a written input, and classifying it based on what the user wants to achieve.

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Annotate data

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.

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Entity

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.

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DSL

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.

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Indexing

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.

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CRUD operations

CRUD stands for Create, Read, Update and Delete, which are the four basic operations of persistent storage.

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Search analyzers

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.

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