Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Log-linear models and more recently neural network models used for supervised relation
extraction requires substantial amounts of training data and time, limiting the portability to …

Two problems arise when using distant supervision for relation extraction. First, in this
method, an already existing knowledge base is heuristically aligned to texts, and the …

Up to now, relation extraction systems have made extensive use of features generated by
linguistic analysis modules. Errors in these features lead to errors of relation detection and …

In 2007, IBM Research took on the grand challenge of building a computer system that could
compete with champions at the game of Jeopardy!™. In 2011, the open-domain question …

Abstract Information-extraction (IE) systems seek to distill semantic relations from
naturallanguage text, but most systems use supervised learning of relation-specific …

The automatic extraction of information from unstructured sources has opened up new
avenues for querying, organizing, and analyzing data by drawing upon the clean semantics …

Previous research on relation classification has verified the effectiveness of using
dependency shortest paths or subtrees. In this paper, we further explore how to make full …