Sentence examples for document representations from inspiring English sources

The annotations are considered part of the document representations.

The relevance links are computed on the basis of the similarity between the user profiles and the document representations.

In this case, training a document embedding as a whole instead of summing up individual term embeddings may result in better document representations, and consequently better retrieval performance.

Wang et al. [26] study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification.

A general schema of our profile-based recommendation approach is depicted in Figure 2. The design consists of a pipeline of processing units that: i) generates user profiles, ii) generates document representations, and iii) computes matching relationships among users and SAD documents.

The user may thus refine the initial query by combining concepts from the offered facets or may pursue the links to the document representations.

Word sense disambiguation can contribute to a better document representation.

This paper suggests an alternative document representation method based on what we call exemplary documents.

The model makes a hard attention through the composition from sentence representation into document representation.

Such document representation schemas consider individual words to be dimensions, with different dimensions being independent.

In our opinion, when computers are used as mediations to existing contents, document representation-based user interfaces are needed.