Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first score indicating a likelihood that a text belongs to a first classification mode by combining a first sentence score and a second sentence score retrieved from an index, the first sentence score indicating a probability that a first sentence in the text belongs to the first classification mode, the second sentence score indicating that a second sentence following the first sentence belongs to the first classification mode, determining a second score indicating a likelihood that the text belongs to a second classification mode, comparing the first score to the second score, classifying the text as the first classification mode when the first score is greater than the second score, and determining a confidence level that the text belongs to the first classification mode by dividing the first score by the second score.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first score indicating a likelihood that a text belongs to a first classification mode by combining a first sentence score and a second sentence score retrieved from an index, the first sentence score indicating a probability that a first sentence in the text belongs to the first classification mode, the second sentence score indicating that a second sentence following the first sentence belongs to the first classification mode, determining a second score indicating a likelihood that the text belongs to a second classification mode, comparing the first score to the second score, classifying the text as the first classification mode when the first score is greater than the second score, and determining a confidence level that the text belongs to the first classification mode by dividing the first score by the second score.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.

Abstract: Methods and apparatus to classify text communications are disclosed. An example method includes determining a first conditional probability of a first feature occurring in a text given that the text belongs to a classification mode, wherein the first feature is included in the text, determining a second conditional probability of a second feature occurring in a text given that the text belongs to the classification mode, wherein the second feature is included in the text, determining a probability of the classification mode occurring, multiplying the first conditional probability, the second conditional probability and the probability of the classification mode to determine a product, and storing the product in a tangible memory as a score that the message belongs to the first classification mode.

Abstract: Example systems, methods, processes, and apparatus identify phrases in electronic information. One or more phrase dictionaries are created from content in one or more electronic documents. A relative frequency value is generated for each phrase in each of the one or more phrase dictionaries. The relative frequency value for a phrase is based at least in part on a comparison between a frequency of the phrase in the electronic document and a frequency of each individual word in the phrase. One or more phrases are selected based at least in part on a threshold and the relative frequency value generated for each phrase. The selected one or more phrases and the relative frequency values associated with each of the selected one or more phrases are output for graphical display to a user.