To advance information extraction and question answering technologies toward a more realistic path, the U.S. NIST (National Institute of Standards and Technology) initiated the KBP (Knowledge Base Population) task as one of the TAC (Text Analysis Conference) evaluation tracks. It aims to encourage research in automatic information extraction of named entities from unstructured texts with the ultimate goal of integrating such information into a structured Knowledge Base. The KBP track consists of two types of evaluation: Named Entity Linking (NEL) and Slot Filling. This paper describes the linguistic resource creation efforts at the Linguistic Data Consortium (LDC) in support of Named Entity Linking evaluation of KBP, focusing on annotation methodologies, process, and features of corpora from 2009 to 2011, with a highlighted analysis of the cross-lingual NEL data. Progressing from monolingual to cross-lingual Entity Linking technologies, the 2011 cross-lingual NEL evaluation targeted multilingual capabilities. Annotation accuracy is presented in comparison with system performance, with promising results from cross-lingual entity linking systems.

In recent months, LDC has developed a web-based annotation infrastructure centered around a tree model of annotations and a Ruby on Rails application called the LDC User Interface (LUI). The effort aims to centralize all annotation into this single platform, which means annotation is always available remotely, with no more software required than a web browser. While the design is monolithic in the sense of handling any number of annotation projects, it is also scalable, as it is distributed over many physical and virtual machines. Furthermore, minimizing customization was a core design principle, and new functionality can be plugged in without writing a full application. The creation and customization of GUIs is itself done through the web interface, without writing code, with the aim of eventually allowing project managers to create a new task without developer intervention. Many of the desirable features follow from the model of annotations as trees, and the operationalization of annotation as tree modification.