Conditional Probabilities

This i)al)er describes a Ilatural language i)ars - ing algorith,n for unrestricted text which uses a prol)al)ility-I~ased scoring function to select the "l)est" i)arse of a sclfl,ence. The parser, T~earl, …...

Statistical language models are useful because they can provide probabilis- tic information upon uncertain decision making. The most common statistic is n-grams measuring word cooccurrences in texts. The method suffers …...

It is shown that for a large corpus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below …...

We describe a system which ranks human- provided paraphrases of noun compounds, where the frequency with which a given paraphrase was provided by human volun- teers is the gold standard …...

AbstractSupervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing …...