document vector造句

例句与造句

1. Similarity search is based comparing document vectors ( see Vector Space Model ).
2. But more importantly we can now treat the term and document vectors as a " semantic space ".
3. However, the computed vectors for the new text are still very relevant for similarity comparisons with all other document vectors.
4. The researchers explain that a " traditional bag-of-words document vector representation " ( counting only word frequencies ) is insufficient.
5. The process of augmenting the document vector spaces for an LSI index with new documents in this manner is called " folding in ".
6. It's difficult to find document vector in a sentence. 用document vector造句挺难的
7. document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as the same kind of vector as the documents.
8. In this model, both documents and queries are represented as vectors of term counts, and the similarity between a document and a query is given by the cosine between the term vector and the document vector.
9. The mnemonic for representing a combination of weights takes the form ddd . qqq, where the first three letters represents the term weighting of the document vector and the second three letters represents the term weighting for the query vector.
10. As all vectors under consideration by this model are elementwise nonnegative, a cosine value of zero means that the query and document vector are orthogonal and have no match ( i . e . the query term does not exist in the document being considered ).
11. Instead, they " employ a deep learning method to obtain a word vector for each word and then apply a sliding window on each document to gradually gain the document vector . " The classifier was trained on a dataset of user pages speedily deleted under criterion " G11.
12. This means that if you have a query vector q, you must do the translation \ hat { \ textbf { q } } = \ Sigma _ k ^ {-1 } U _ k ^ T \ textbf { q } before you compare it with the document vectors in the low-dimensional space.