1. A method comprising: performing, by computer processing hardware, operations of: analyzing a distribution of repeat occurrences of words in a word pool to train a class-based language model; creating the class-based language model to include multiple classes of words, the multiple classes of words including at least a first class of words and a second class of words; identifying a word present in the word pool; assigning the word to the first class of words; assigning the word to the second class of words; and depending on the distribution of the repeat occurrences of the words in the word pool, generating probability information associated with the multiple classes to create the class-based language model; wherein the word assigned to the first class is used in accordance with a first meaning in the word pool, the first class of words including a first set of words from the word pool that are commonly used together in a first context; wherein the word assigned to the second class is used in accordance with a second meaning in the word pool, the second class of words including a second set of words from the word pool that are commonly used together in a second context; the method further comprising: receiving a sample sequence of words; utilizing the class-based language model to identify an overall probability value indicative of how likely an instance of the word follows the sample sequence of words; and wherein utilizing the class-based language model to identify the overall probability value includes: in response to detecting that there is no occurrence in which the word in the first class follows an instance of the sample sequence in the word pool: i) producing a modified sample sequence by removing at least one word from the sample sequence; ii) generating a first probability value based at least in part on a probability that the word in the first class follows an occurrence of the modified sample sequence in the word pool; and in response to detecting that there is at least one occurrence in which the word in the second class follows the sample sequence in the word pool, generating a second probability value based at least in part on a number of occurrences in which the word in the second class follows an occurrence of the sample sequence in the word pool.
1. A method comprising: performing, by computer processing hardware, operations of: analyzing a distribution of repeat occurrences of words in a word pool to train a class-based language model; creating the class-based language model to include multiple classes of words, the multiple classes of words including at least a first class of words and a second class of words; identifying a word present in the word pool; assigning the word to the first class of words; assigning the word to the second class of words; and depending on the distribution of the repeat occurrences of the words in the word pool, generating probability information associated with the multiple classes to create the class-based language model; wherein the word assigned to the first class is used in accordance with a first meaning in the word pool, the first class of words including a first set of words from the word pool that are commonly used together in a first context; wherein the word assigned to the second class is used in accordance with a second meaning in the word pool, the second class of words including a second set of words from the word pool that are commonly used together in a second context; the method further comprising: receiving a sample sequence of words; utilizing the class-based language model to identify an overall probability value indicative of how likely an instance of the word follows the sample sequence of words; and wherein utilizing the class-based language model to identify the overall probability value includes: in response to detecting that there is no occurrence in which the word in the first class follows an instance of the sample sequence in the word pool: i) producing a modified sample sequence by removing at least one word from the sample sequence; ii) generating a first probability value based at least in part on a probability that the word in the first class follows an occurrence of the modified sample sequence in the word pool; and in response to detecting that there is at least one occurrence in which the word in the second class follows the sample sequence in the word pool, generating a second probability value based at least in part on a number of occurrences in which the word in the second class follows an occurrence of the sample sequence in the word pool. 3. The method as in claim 1 further comprising: for a particular word sequence in the word pool: identifying classes from the multiple classes that include a corresponding word that follows an occurrence of the particular word sequence in the word pool; generating a class probability value indicating a likelihood that at least one word in each of the identified classes occurs after the particular word sequence; and assigning the class probability values to the identified classes.