Word embedding
Technique in natural language processing that represents words as vectors in a continuous vector space
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.
Nº Q18395344 ★★
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Word embedding
Technique in natural language processing that represents words as vectors in a continuous vector space
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.
Last price
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Floor price
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7-day median
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30-day sales
0
30-day range
—
In circulation
0
Price history
median
low – high
sales
No sales in this period
Show table
| Date | median | Low | High | sales |
|---|
Sales history
- Last sale
- —
- 30-day average
- —
- 30-day low
- —
- 30-day high
- —
- Sales 7d
- 0
- Sales 30d
- 0
No sales yet.
Anonymous sales: no buyer or seller shown. Figures count player-to-player sales only.
From Wikipedia
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. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear. Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.
Text: Wikipédia, CC BY-SA 4.0. · Image: Fschwarzentruber (CC BY-SA 4.0) ·
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