What principle underlies context independent embeddings?
A. Representing words solely based on their semantic meaning.
B. Capturing the relationship between words in a sentence.
C. Considering the surrounding context for word representation.
D. Using external knowledge sources like dictionaries.
Answer (2)
The principle of context-independent embeddings is based on representing words solely based on their semantic meaning, where each word has a fixed vector representation. This approach contrasts with context-dependent embeddings, which vary based on surrounding words. Therefore, the correct answer is A.
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The principle underlying context-independent embeddings is (A) Representing words solely based on their semantic meaning.
In the field of natural language processing (NLP) and computational linguistics, context-independent embeddings are a type of word representation. They do not take into account the surrounding words or context in which a word appears. Instead, each word is assigned a fixed vector regardless of where or how it is used in sentences. Popular models that use this approach are Word2Vec and GloVe.
Here's how it works:
Semantic Representation : Each word is mapped to a vector in a high-dimensional space. This vector is designed to capture the intrinsic semantic meaning of the word based on its co-occurrence statistics with other words in large corpora of text. Words with similar meanings are placed close together in this vector space.
Training : The embeddings are typically learned by training on large datasets where words are represented by fixed-length vectors. The learning process involves capturing patterns of word usages from the corpus.
Limitations : The main limitation of context-independent embeddings is their inability to account for polysemy—a single word having multiple meanings. Since the embedding is fixed, it cannot distinguish between different meanings in different contexts.
In summary, context-independent embeddings aim to capture the general meaning of words based on their usual context across a vast body of text, but do not adjust for specific contextual nuances in individual sentences.
