Sentence, Text, Word and Document Embeddings - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In order to analyze text and execute algorithms, it is essential to transform the text into a vectorized format. Word embeddings describe the technique of representing words as vectors in a continuous vector space, where the positioning of vectors captures semantic relationships between words based on their contextual usage. The goal of word embeddings is to map words from a discrete and high-dimensional space (vocabulary) to a continuous and lower-dimensional vector space. The resulting word vectors are expected to encode semantic information, and similar words are represented by vectors that are close to each other in the space. The concept of embedding essentially involves converting the given textual input into a collection of numerical vectors, which can then be seamlessly integrated into algorithmic processes. Various methodologies for achieving this will be elaborated upon in forthcoming articles. To do this, we emphasize the need to represent the text as a vector:
Word embedding techniques, such as Word2Vec, GloVe, and FastText, aim to capture the meanings of words and their relationships by considering the distributional patterns in large corpora of text. SentenceTransformers, as shown in 2382, is a framework for state-of-the-art sentence, text and image embeddings in Python. Figure 2382. SentenceTransformers. ============================================
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