[3][4] Determining whether this relationship holds is an informal task, one which sometimes overlaps with the formal tasks of formal semantics (satisfying a strict condition will usually imply satisfaction of a less strict conditioned); additionally, textual entailment partially subsumes word entailment.
This variability of semantic expression can be seen as the dual problem of language ambiguity.
[4] 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.
[6] Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate.
[8] 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.