on the directional property of this relation, by making a comparison between some directional similarities of the texts involved. Approaches Textual entailment measures natural language understanding as it asks for a semantic interpretation of the text, and due to its generality remains an active area of research. Many approaches and refinements of approaches have been considered, such as word embedding, logical models, graphical models, rule systems, contextual focusing, and machine learning. Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate. However, even state-of-the-art systems are still far from human performance; a study found humans to be in agreement on the dataset 95.25% of the time, while algorithms from 2016 had not yet achieved 90%. Applications Many natural language processing applications, like question answering, information extraction, summarization, multi-document summarization, and evaluation of machine translation systems, need to recognize that a particular target meaning can be inferred from different text variants. Typically entailment is used as part of a larger system, for example in a prediction system to filter out trivial or obvious predictions. Textual entailment also has applications in adversarial stylometry, which has the objective of removing textual style without changing the overall meaning of communication. See also Inference engine Semantic reasoner Fuzzy logic References Bibliography External links Textual Entailment Resource Pool Tasks of natural language processing Logical consequence