What can word Embeddings be used for?

A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems.

Where are word Embeddings used?

A common practice in NLP is the use of pre-trained vector representations of words, also known as embeddings, for all sorts of down-stream tasks. Intuitively, these word embeddings represent implicit relationships between words that are useful when training on data that can benefit from contextual information.

What is the advantage of word Embeddings over traditional methods such as bag of words?

Word embeddings may increase the accuracy of classification models as they provide information (and vector representations) on words that are not or only scarcely represented in the training data based on their similarity with other words (Goldberg, 2016).

What is the disadvantage of Bag of Words Embeddings?

Drawbacks of using a Bag-of-Words (BoW) Model If the new sentences contain new words, then our vocabulary size would increase and thereby, the length of the vectors would increase too. Additionally, the vectors would also contain many 0s, thereby resulting in a sparse matrix (which is what we would like to avoid)

How do word Embeddings capture meaning?

the embeddings capture the sentence’s semantics. In other. words, for successful application to these areas it is required. that the embeddings generated by the models correctly en- code meaning such that sentences with the same meaning.

Why are Embeddings useful?

Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models.

What are the advantages and disadvantages of GloVe compared to Word2Vec?

The advantage of GloVe is that, unlike Word2vec, GloVe does not rely just on local statistics (local context information of words), but incorporates global statistics (word co-occurrence) to obtain word vectors.

Why is Bag of Words bad?

bag of words has two major issues: it has the curse of dimensionality issue as the total dimension is the vocabulary size. It can easily over-fit your model. The remedy is to use some well-known dimensionality reduction technique to your input data.