Sentence examples for most language models from inspiring English sources
In most language models, individual words are not equally significant markers of the property being considered, but summing across rows implicitly assumes that they are.
The simplest and most successful statistical language models are the Markov chain (n-gram) source models, first explored by Shannon [43].
Most notably, current approaches abandon the feature calculations, timing models, and language models of yore, replacing them all with a single deep neural network.
In this chapter, we first introduce the concept of language models (Section 12.1 ) and then describe the basic and most commonly used language modeling approach to IR, the Query Likelihood Model (Section 12.2 ).
And there are some language-related updates too — with enhancements to Swype's Chinese and Japanese keyboards, and more languages getting its Advanced Language Models which is used to predict the words and phrases Swype users write the most.
Relevance-based language models.
Most language families are represented by only one language.
A language models with low perplexity indicate more predictable language.
While they were always beneficial in our experiments, a tagger trained with the neural language model has most substantial performance improvements in OOV words.
The language model in most state-of-the-art LVCSR systems is still the N-gram, which assigns probability to the next word based on only the N−1 preceding words [64].
