Embeddings Slides | Learn Importance Of Embeddings For NLP, LLM, AI Assistants

Here is a comprehensive understanding of embeddings in Natural Language Processing (NLP), explaining their role in representing words with similar meanings in a similar way and capturing complex relationships between different words. It discusses the creation of embeddings using methods like neural networks and dimensionality reduction, the significance of dimensions in embeddings, and the trade-off between dimensionality and model performance.
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Understanding Embeddings in Natural Language Processing (NLP)

Topic Description
What are Embeddings? Embeddings are a type of word representation that allows words with similar meaning to have a similar representation. They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. Embeddings are a way of using position and distance in vector space to represent the way words relate to each other in the real world.
Why are they used in NLP? Embeddings are used in NLP to capture not just the semantic meaning of individual words, but also the complex relationships between different words. They allow us to overcome the limitations of bag-of-words models, which ignore the order of words and therefore the context in which they appear. Embeddings, on the other hand, provide a dense representation where different words with similar context will have a similar vector.
How are Embeddings created? Embeddings are created using various methods. The most common methods include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, or explicitly encoding knowledge about relations between words. Word2Vec, GloVe, and FastText are some of the most popular models to generate embeddings in NLP.
What are dimensions in Embeddings? Dimensions in embeddings represent the size of the embedding vectors. Each dimension in the vector can be considered as a feature of the word. The number of dimensions is a parameter you can set. A higher number of dimensions allows the embedding to capture more nuanced relationships between words, but also requires more data to learn effectively.
What do they represent? Each dimension in the embedding vector can represent a latent semantic feature of the word. For example, one dimension may capture the gender property of words (like king vs queen), another dimension may capture the tense of verbs (like walk vs walked), and so on. However, these dimensions are not explicitly interpretable in most cases.
The relationship between dimensionality and model performance There is a trade-off between the dimensionality of the embeddings and the model performance. While higher dimensions can capture more information and thus potentially improve the model performance, it also increases the computational complexity and the risk of overfitting, especially when the amount of data is limited. Therefore, choosing the right dimensionality is crucial for the performance of NLP models.



Build-a-custom-rag-pipeline-w    Building-a-recommendation-sys    Challenges-in-good-embeddings    Chunking-and-tokenization    Chunking    Clip-and-multimodal-embedding    Compression-techniques-for-em    Dimensionality-reduction-need    Dimensionality-vs-model-perfo    Embedding-applications-in-e-c   

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