Method and configuration for forming classes for a language model based on linguistic classes

A method for forming classes for a language model based on linguistic classes using a computer, which comprises the steps of:

• using a first mapping rule to determine N classes using a prescribed vocabulary with associated linguistic properties;
• determining K classes from the N classes by minimizing a language model entropy;
• using the K classes to represent a second mapping rule for forming the classes of language models onto the linguistic classes;
• and
• determining the language model entropy by use of equation H ⁡ ( LM ) = - 1 n · log ⁢ ⁢ P ⁡ ( W ) ,
• where
• H(LM) denotes the language model entropy of the language model,
• n denotes a number of words in a text,
• W denotes a chain of the words w0, w1, . . . , wn, and
• P(W) denotes a probability of an occurrence of a sequence of at least two of the words.

A. method for forming classes for a language model based on linguistic classes using a computer, which comprises the steps of:

• using a first mapping rule to prescribe N classes;
• determining IC classes from the N classes by minimizing a language model entropy, including:
• determining a number M of most probable of the N classes as base classes;
• and
• merging one of remaining classes (N−M) of the classes with one of the base classes for which the language model entropy is minimized;
• and
• using the K classes to represent a second mapping rule for forming the classes of language models onto the linguistic classes.

A method for forming classes for a language model based on linguistic classes Using a computer, which comprises the steps of:

• using a first mapping rule to prescribe N classes;
• determining K classes from the N classes by minimizing a language model entropy;
• using the K classes to represent a second mapping rule for forming the classes of language models onto the linguistic classes;
• and
• determining the language model entropy by use of equation H ⁡ ( LM ) = - 1 n · log ⁢ ⁢ P ⁡ ( W ) ,
• where
• H(LM) denotes the language model entropy of the language model,
• n denotes a number of words in a texts
• W denotes a chain of the words w0, w1, . . . , wn, and
• P(W) denotes a probability of an occurrence of a sequence of at least two of the words.