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Job embeddedness was first introduced by Mitchell and colleagues [1] in an effort to improve traditional employee turnover models. According to these models, factors such as job satisfaction and organizational commitment and the individual's perception of job alternatives together predict an employee's intent to leave and subsequently, turnover (e.g., [4] [5] [6] [7]).
TransE embedding model. The vector representation (embedding) of the head plus the vector representation of the relation should be equal to the vector representation of the tail entity. TransE [9]: Uses a scoring function that forces the embeddings to satisfy a simple vector sum equation in each fact in which they appear: + =. [7]
An alternative motivation theory to Maslow's hierarchy of needs is the motivator-hygiene (Herzberg's) theory. While Maslow's hierarchy implies the addition or removal of the same need stimuli will enhance or detract from the employee's satisfaction, Herzberg's findings indicate that factors garnering job satisfaction are separate from factors leading to poor job satisfaction and employee turnover.
According to the authors' note, [3] CBOW is faster while skip-gram does a better job for infrequent words. After the model is trained, the learned word embeddings are positioned in the vector space such that words that share common contexts in the corpus — that is, words that are semantically and syntactically similar — are located close to ...
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. [1] It was created by researchers at the Allen Institute for Artificial Intelligence , [ 2 ] and University of Washington and first released in February, 2018.
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.
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]