Readers: Evaluation and Development of Reading Systems

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Machine reading aims to extract knowledge from unstructured text with little human effort. It has been a major goal of AI since its early days. The ever growing amounts of textual data available over the internet further increase the importance and urgency of computer-based methods for knowledge extraction. The success of machine reading will not only help breach the knowledge acquisition bottleneck in AI, but also revolutionize Web search, information extraction, and the automatic construction of resources such as Wikipedia. In the past, there has been a lot of progress in automating many substasks of machine reading using standard NLP technology such as tagging and parsing. However, end-to-end solutions are still rare, and existing systems typically require substantial human effort in manual engineering and/or labeling examples. As a result, they often target restricted domains and only extract limited types of knowledge (e.g., a pre-specified relation). In this project we aim to develop an end-to-end system that operates over raw text, extracts knowledge and is able to answer questions and support other end tasks. A key insight in our approach is the use of unsupervised methods that do not rely on large amounts of hand annotation for the acquisition of background knowledge, its linking to existing knowledge bases, and the creation of new ones. Our approach will acquire knowledge at Web-scale, be open to arbitrary domains, genres, and languages.It will constantly integrate new information sources (e.g., new text documents) and learn from user questions and feedback (e.g., via performing end tasks).