Identifying technologies is a key element for mapping a domain and its evolution. It allows
managers and decision makers to anticipate trends for an accurate forecast and effective …

Texts with potential educational value are becoming available through the Internet (eg,
Wikipedia, news services). However, using these new tests in classrooms introduces many …

Supervised approaches to named entity recognition (NER) are largely developed based on
the assumption that the training data is fully annotated with named entity information …

Many researchers are trying to use information extraction (IE) to create large-scale
knowledge bases from natural language text on the Web. However, the primary approach …

Abstract We present Expected Statistic Regulariza tion (ESR), a novel regularization
technique that utilizes low-order multi-task structural statistics to shape model distributions …

Advances in machine reading comprehension (MRC) rely heavily on the collection of large
scale human-annotated examples in the form of (question, paragraph, answer) triples. In …

This paper addresses the task of assigning labels of fine-grained named entity (NE) types to
Wikipedia articles. Information of NE types are useful when extracting knowledge of NEs …

We study learning named entity recognizers in the presence of missing entity annotations.
We approach this setting as tagging with latent variables and propose a novel loss, the …

A geo-parser automatically identifies location words in a text. We have generated a geo-
parser specifically to find locations in unstructured Spanish text. Our novel geo-parser …

Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is
well-established, there is no universally used tagset: often, datasets are annotated for use in …