This article discusses Open Mind Word Expert (OMWE), a system that aims to tap people's ability to disambiguate words and to give computers the benefit of people's knowledge.

Presentation for the 2014 International Internet Preservation Consortium (IIPC) General Assembly. This presentation discusses building a collection of web archiving research articles.

This paper describes a dialogue system framework for a companionable robot, which aims to guide patients towards health behavior changes via natural language analysis and generation.

Article discussing parallel texts and natural language processing.

Paper on mapping texts and combining text-mining and geo-visualization to unlock the research potential of historical newspapers.

Paper for the 2014 Association for Information Science and Technology (ASIS&T) Annual Meeting. This paper discusses building a collection of web archiving research articles.

Paper on co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community.

This paper discusses letter level learning for language independent diacritics restoration.

Article summarizing the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials, (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities.

This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment which was organized as part of the Association for Computational Linguistics (ACL) 2005 Workshop on Building and Using Parallel Texts. The shared task included English-Inuktitut, Romanian-English, and English-Hindi sub-tasks, and drew the participation of ten teams from around the world with a total of 50 systems.

This paper discusses an evaluation exercise for word alignment.

Article describes how embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images.