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Machine learning based query term weight and synonym analyzer for query expansion. LucQE - open-source, Java. Provides a framework along with several implementations that allow to perform query expansion with the use of Apache Lucene. Xapian is an open-source search library which includes support for query expansion; ReQue open-source, Python ...
Data Analysis Expressions (DAX) is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services (SSAS) Tabular models. DAX includes some of the functions that are used in Excel formulas with additional functions that are designed to work with relational data and perform dynamic aggregation.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
We can generalize the previous 2D extended Boolean model example to higher t-dimensional space using Euclidean distances. This can be done using P-norms which extends the notion of distance to include p-distances, where 1 ≤ p ≤ ∞ is a new parameter.
In the formula, and are the vectors used to iterate through the two sets and and form vector summations. These sums are normalized, i.e. divided by the size of their respective document set. These sums are normalized, i.e. divided by the size of their respective document set.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Image source: The Motley Fool. Dynatrace (NYSE: DT) Q3 2025 Earnings Call Jan 30, 2025, 8:00 a.m. ET. Contents: Prepared Remarks. Questions and Answers. Call ...
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.