Sentence examples for document pair from inspiring English sources

In this chapter we initiate the study of assigning a score to a (query, document) pair.

For tiny collections like Cranfield, exhaustive judgments of relevance for each query and document pair were obtained.

A gain-directed document pair selection method that measures how much we can learn by revealing judgments of selected document pairs is designed.

Notice that there will be a line of output for EACH document in which that word appears and EACH word and document pair should only have ONE list of indices.

We now invoke the following trick to detect document pairs whose sets have large Jaccard overlaps.

This paper investigates a framework that actively selects informative document pairs for obtaining user feedback for semi-supervised document clustering.

We use the estimation of term co-occurrence probabilities as a clue for finding informative document pairs.

Specifically, the loss functions are defined on the level of documents or document pairs, in contrast to the fact that the evaluation criteria are defined on the level of queries.

In the topic model, we used the original and muted document pairs as positive samples and the original and random document pairs as negative samples to train the model.

We present a method to identify new translations of both known and unknown source language words that uses NEW-domain comparable document pairs.

Starting with a joint distribution of source-target word pairs derived from the OLD-domain parallel corpus, our method recovers a new joint distribution that matches the marginal distributions of the NEW-domain comparable document pairs, while minimizing the divergence from the OLD-domain distribution.