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. 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%. Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate. Many approaches and refinements of approaches have been considered, such as word embedding, logical models, graphical models, rule systems, contextual focusing, and machine learning. 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. Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarities of the texts involved. ![]() Textual entailment is similar but weakens the relationship to be unidirectional. The task of paraphrasing involves recognizing when two texts have the same meaning and creating a similar or shorter text that conveys almost the same information. Together, they result in a many-to-many mapping between language expressions and meanings. This variability of semantic expression can be seen as the dual problem of language ambiguity. Ambiguity of natural language Ī characteristic of natural language is that there are many different ways to state what one wants to say: several meanings can be contained in a single text and that the same meaning can be expressed by different texts. Hypothesis: Giving money to a poor man will make you a better person. Hypothesis: Giving money to a poor man has no consequences.Īn example of a non-TE (text does not entail nor contradict) is: Hypothesis: Giving money to a poor man has good consequences.Īn example of a negative TE (text contradicts hypothesis) is:
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