Applications with Context Vectors Explored

Context vectors offer a significant advancement in natural language processing (NLP) tasks by capturing the contextual meaning of words. They help in identifying the correct sense of a word in a sentence, especially when it possesses multiple meanings. The exploration of context vectors reveals their application in various NLP tasks such as contextual keyword extraction and extractive summarization.

Contextual keyword extraction identifies key words in a document based on their contextual relevance. This technique highlights representative words by determining which words are semantically similar to the document. It is applicable in tasks like information retrieval, document clustering, and text summarization. A simple system can be implemented using a model like BERT to generate context vectors for each word in the document.

To compute the document vector, the average of all token vectors is taken, or one can extract the [CLS] prefix token after processing the entire document. However, if the document is lengthy, it may be split into sentences for separate processing. By calculating the cosine similarity between each word vector and the document vector, one can identify the top N words that best represent the document.

The cosine similarity metric indicates how close two vectors are. If a word vector is near the document vector, it is likely a good representative. This method leverages the semantic understanding embedded in the transformer model, differing from traditional methods based on frequency or predefined rules.

Summarizing documents can be done through extractive summarization, which involves selecting the most representative sentences. This method is straightforward with context vectors, as sentence and document embeddings can be generated easily. The sentences most similar to the document are selected based on their cosine similarity scores, forming a coherent summary of the text.