This paper introduces the parallel Chinese-English Entities, Relations and Events (ERE) corpora developed by Linguistic Data Consortium under the DARPA Deep Exploration and Filtering of Text (DEFT) Program. Original Chinese newswire and discussion forum documents are annotated for two versions of the ERE task. The texts are manually translated into English and then annotated for the same ERE tasks on the English translation, resulting in a rich parallel resource that has utility for performers within the DEFT program, for participants in NIST’s Knowledge Base Population evaluations, and for cross-language projection research more generally.

The DARPA BOLT Program develops systems capable of allowing English speakers to retrieve and understand information from informal foreign language genres. Phase 2 of the program required large volumes of naturally occurring informal text (SMS) and chat messages from individual users in multiple languages to support evaluation of machine translation systems. We describe the design and implementation of a robust collection system capable of capturing both live and archived SMS and chat conversations from willing participants. We also discuss the challenges recruitment at a time when potential participants have acute and growing concerns about their personal privacy in the realm of digital communication, and we outline the techniques adopted to confront those challenges. Finally, we review the properties of the resulting BOLT Phase 2 Corpus, which comprises over 6.5 million words of naturally-occurring chat and SMS in English, Chinese and Egyptian Arabic.

We describe efforts to create corpora to support development and evaluation of handwriting recognition and translation technology. LDC has developed a stable pipeline and infrastructures for collecting and annotating handwriting linguistic resources to support the evaluation of MADCAT and OpenHaRT. We collect and annotate handwritten samples of pre-processed Arabic and Chinese data that has been already translated in English that is used in the GALE program. To date, LDC has recruited more than 600 scribes and collected, annotated and released more than 225,000 handwriting images. Most linguistic resources created for these programs will be made available to the larger research community by publishing in LDC's catalog. The phase 1 MADCAT corpus is now available.

Statistical Machine Translation (MT) systems have achieved impressive results in recent years, due in large part to the increasing availability of parallel text for system training and development. This paper describes recent efforts at Linguistic Data Consortium to create linguistic resources for MT, including corpora, specifications and resource infrastructure. We review LDC's three-pronged ap-proach to parallel text corpus development (acquisition of existing parallel text from known repositories, harvesting and aligning of potential parallel documents from the web, and manual creation of parallel text by professional translators), and describe recent adap-tations that have enabled significant expansions in the scope, variety, quality, efficiency and cost-effectiveness of translation resource creation at LDC.

The NIST Automatic Content Extraction (ACE) Evaluation expands its focus in 2008 to encompass the challenge of cross-document and cross-language global integration and reconciliation of information. While past ACE evaluations have been limited to local (within-document) detection and disambiguation of entities, relations and events, the current evaluation adds global (cross-document and cross-language) entity disambiguation tasks for Arabic and English. This paper presents the 2008 ACE XDoc evaluation task and associated infrastructure. We describe the linguistic resources created by LDC to support the evaluation, focusing on new approaches required for data selection, data processing, annotation task definitions and annotation software, and we conclude with a discussion of the metrics developed by NIST to support the evaluation.

Entities - people, organizations, locations and the like - have long been a central focus of natural language processing technology development, since entities convey essential content in human languages. For multilingual systems, accurate translation of named entities and their descriptors is critical. LDC produced Entity Translation pilot data to support the ACE ET 2007 Evaluation and the current paper delves more deeply into the entity alignment issue across languages, combining the automatic alignment techniques developed for ACE-07 with manual alignment. Altogether 84% of the Chinese-English entity mentions and 74% of the Arabic-English entity mentions are perfect aligned. The results of this investigation offer several important insights. Automatic alignment algorithms predicted that perfect alignment for the ET corpus was likely to be no greater than 55%; perfect alignment on the 15 pilot documents was predicted at 62.5%. Our results suggest the actual perfect alignment rate is substantially higher (82% average, 92% for NAM entities). The careful analysis of alignment errors also suggests strategies for human translation to support the ET task; for instance, translators might be given additional guidance about preferred treatments of name versus nominal translation. These results can also contribute to refined methods of evaluating ET systems.