Text-Item Embedding Algorithm
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A Text-Item Embedding Algorithm is a neural enconding algorithm that can be implemented by a Text-Item Embedding System to solve a Text-Item Embedding Task (which requires the transformation of text items into dense text-item vector representations).
- Context:
- It can (typically) learn to represent text items as vectors such that the geometric relationships between these vectors correspond to semantic relationships between the text items.
- It can (typically) use deep learning models, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Transformer models, and Autoencoders, to generate embeddings.
- It can (often) be trained unsupervised using large text corpora to capture a wide array of linguistic patterns and nuances.
- It can (often) incorporate context and order information, providing a more nuanced understanding of text than bag-of-words models.
- ...
- Example(s):
- a Word Embedding Algorithm, such as:
- Word2Vec, an algorithm that produces word embeddings by predicting a word from its context or predicting the context from a word.
- GloVe (Global Vectors for Word Representation), which generates word embeddings by aggregating global word-word co-occurrence statistics from a corpus.
- BERT (Bidirectional Encoder Representations from Transformers), which offers deep contextualized embeddings by considering both left and right context in all layers of the model.
- a Sentence Embedding Algorithm, such as:
- S-BERT.
- ...
- a Document Embedding Algorithm, such as:
- Doc2Vec, an extension of Word2Vec that generates embeddings for sentences or documents.
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- a Word Embedding Algorithm, such as:
- Counter-Example(s):
- A Feature Engineering Technique that relies on manually crafted features rather than learning dense vector representations.
- A Rule-Based Natural Language Processing System, which operates based on predefined linguistic rules without learning from data.
- See: Semantic Similarity, Natural Language Processing, Deep Learning, Machine Learning.