Textual entailment in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text and hypothesis, respectively. Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "t entails h" if, typically, a human reading t would infer that h is most likely true. The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain. Determining whether this relationship holds is an informal task, one which sometimes overlaps with the formal tasks of formal semantics ; additionally, textual entailment partially subsumes word entailment.
Examples
Textual entailment can be illustrated with examples of three different relations: An example of a positive TE is:
A 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. This variability of semantic expression can be seen as the dual problem of language ambiguity. Together, they result in a many-to-many mapping between language expressions and meanings. 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. Textual entailment is similar but weakens the relationship to be unidirectional. 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.
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%.
