But how does this bear on meaning representation of natural language sentences and texts? In essence, the representation  of sentences in statistical semantics consists of the sentences themselves. The idea that sentences can be used directly, in conjunction with distributional knowledge, as objects enabling inference is a rather recent and surprising one, though it was foreshadowed by many years of work on question answering based on large text corpora. The idea has gained traction as a result of recent efforts to devise statistically based algorithms for determining textual entailment, a program pushed forward by a series of Recognizing Textual Entailment (RTE) Challenges initiated in 2005, organized by the PASCAL Network of Excellence, and more recently by the National Institute of Standards and Technology (NIST). Recognizing textual entailment requires judgments as to whether one given linguistic string entails a second one, in a sense of entailment that accords with human intuitions about what a person would naturally infer (with reliance on knowledge about word meanings, general knowledge such as that any person who works for a branch of a company also works for that company, and occasional well-known specific facts). For example,  “John is a fluent French speaker” textually entails  “John speaks French”, while  “The gastronomic capital of France is Lyon” does not entail that  “The capital of France is Lyon”. Some examples are intermediate; e.g.,  “John was born in France” is considered to heighten the probability that John speaks French, without fully entailing it (Glickman and Dagan 2005). Initial results in the annual competitions were poor (not far above the random guessing mark), but have steadily improved, particularly with the injection of some reasoning based on ontologies and on some general axioms about the meanings of words, word classes,  relations, and phrasal patterns (e.g., de Salvo Braz et al. 2005).