Search results
Results From The WOW.Com Content Network
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
The CBOW can be viewed as a ‘fill in the blank’ task, where the word embedding represents the way the word influences the relative probabilities of other words in the context window. Words which are semantically similar should influence these probabilities in similar ways, because semantically similar words should be used in similar contexts.
static is a reserved word in many programming languages to modify a declaration. The effect of the keyword varies depending on the details of the specific programming language, most commonly used to modify the lifetime (as a static variable) and visibility (depending on linkage), or to specify a class member instead of an instance member in classes.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.
Structural typing is a static typing system that determines type compatibility and equivalence by a type's structure, whereas duck typing is dynamic and determines type compatibility by only that part of a type's structure that is accessed during runtime. The TypeScript, [6] Elm [7] and Python [8] languages support structural typing to varying ...
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold .