Active learning is a supervised machine learning technique in which the learner is in control
of the data used for learning. That control is utilized by the learner to ask an oracle, typically …

Active learning promises to alleviate the massive data needs of supervised machine
learning: it has successfully improved sample efficiency by an order of magnitude on …

Active learning aims to train an accurate prediction model with minimum cost by labeling
most informative instances. In this paper, we survey existing works on active learning from …

The KnowItAll system aims to automate the tedious process of extracting large collections of
facts (eg, names of scientists or politicians) from the Web in an unsupervised, domain …

US8595222B2 - Methods and systems for representing, using and displaying time-varying
information on the semantic web - Google Patents US8595222B2 - Methods and systems for …

In this chapter we present a brief overview of Information Extraction, which is an area of
natural language processing that deals with finding factual information in free text. In formal …

Active learners alleviate the burden of labeling large amounts of data by detecting and
asking the user to label only the most informative examples in the domain. We focus here on …

In this paper, we describe the construction of a semantically annotated corpus of clinical
texts for use in the development and evaluation of systems for automatically extracting …

Text mining is a very exciting research area as it tries to discover knowledge from
unstructured texts. These texts can be found on a computer desktop, intranets and the …

Active learning has become an important area of research owing to the increasing number
of real-world problems in which a huge amount of unlabelled data is available. Active …