Sie sind hier

Recall-aware Information Extraction

Ms. Nitisha Jain

Most information extraction (IE) systems are designed to construct Knowledge Bases (KBs) consisting of high precision facts. The focus is primarily on the confidence in the correctness of the data. As KBs are being increasingly considered to be representations of the real world, it is imperative that the data in KBs is not only correct, but also complete. Yet, most widely used KBs today do not store the completeness information for many common predicates such as names of children or winners of an award. This incompleteness of KB facts is the result of oversight on the part of most information extraction processes, that emphasize on the optimization of precision, but largely ignore the recall. A recall oriented IE system is highly desirable for many use cases. Recall oriented IE systems can help identify the parts of KBs that are incomplete and enable efforts to improve the overall completeness of KBs. Many dependent downstream applications such as question answering, machine translation and text comprehension, which require KB facts to be correct and comprehensive, can also benefit from this approach. This talk discusses whether it is possible to make IE recall-aware and possible cues towards estimating the recall.

21.08.2018 - 10:15
B 016