Systematic reviews require expert reviewers to manually screen thousands of citations in
order to identify all relevant articles to the review. Active learning text classification is a …

How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …

TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …

The success of recommender systems often depends on their ability to understand and
make use of the context of the recommendation request. Significant research has focused on …

Correctly detecting the semantic type of data columns is crucial for data science tasks such
as automated data cleaning, schema matching, and data discovery. Existing data …

For evaluating machine-generated texts, automatic methods hold the promise of avoiding
collection of human judgments, which can be expensive and time-consuming. The most …

Documents exhibit sequential structure at multiple levels of abstraction (eg, sentences,
paragraphs, sections). These abstractions constitute a natural hierarchy for representing the …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …

In document-level sentiment classification, each document must be mapped to a fixed length
vector. Document embedding models map each document to a dense, low-dimensional …

Two document representation methods are mainly used in solving text mining problems.
Known for its intuitive and simple interpretability, the bag-of-words method represents a …