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Debiasing Word Embeddings” that a publicly available (and popular) word2vec embedding trained on Google News texts (a commonly used data corpus), which consists of text written by professional journalists, still shows disproportionate word associations reflecting gender and racial biases when extracting word analogies. [55] For example, one ...
The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. R. Firth's distributional hypothesis ...
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.
For example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and ...
The creation of human-annotated treebanks using various formalisms (e.g. Universal Dependencies) has proceeded alongside the development of new algorithms and methods for parsing. Part-of-speech tagging (which resolves some semantic ambiguity) is a related problem, and often a prerequisite for or a subproblem of syntactic parsing.
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.
Examples of common tokens Token name (Lexical category) Explanation Sample token values identifier: Names assigned by the programmer. x, color, UP: keyword: Reserved words of the language. if, while, return: separator/punctuator: Punctuation characters and paired delimiters.}, (, ; operator: Symbols that operate on arguments and produce results ...
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]