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Unlike static word embeddings, these embeddings are at the token-level, in that each occurrence of a word has its own embedding. These embeddings better reflect the multi-sense nature of words, because occurrences of a word in similar contexts are situated in similar regions of BERT’s embedding space. [41] [42]
It consists of clustering words, which are semantically similar and can thus bear a specific meaning. Lin’s algorithm [5] is a prototypical example of word clustering, which is based on syntactic dependency statistics, which occur in a corpus to produce sets of words for each discovered sense of a target word. [6]
For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target ...
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 ...
In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word " play ", each of these having a different meaning based on the context of the word's usage in a sentence , as follows:
Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or ...
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 .
The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .