Abstract: An automatic rule generation system generates rules for fact extraction. A rule generation module receives a sample and generates a rule from the sample. A rule relaxation module generates a relaxed rule from the rule. A rule testing module generates a reverse index from a corpus, applies the relaxed rule to the reverse index, and generates text segments. An information extraction module generates modified text segments from the relaxed rule and the text segments. A candidate suggestion module performs a candidate generation process: if the candidate generation process generates no candidates, the candidate suggestion module signals the rule relaxation module to generate a further relaxed rule to use as the relaxed rule. A user evaluates a candidate and provides the candidate as an additional sample for the automatic rule generation system to generate another rule to use as the rule.

Abstract: A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus information.

Abstract: An automatic rule generation system generates rules for fact extraction. A rule generation module receives a sample and generates a rule from the sample. A rule relaxation module generates a relaxed rule from the rule. A rule testing module generates a reverse index from a corpus, applies the relaxed rule to the reverse index, and generates text segments. An information extraction module generates modified text segments from the relaxed rule and the text segments. A candidate suggestion module performs a candidate generation process: if the candidate generation process generates no candidates, the candidate suggestion module signals the rule relaxation module to generate a further relaxed rule to use as the relaxed rule. A user evaluates a candidate and provides the candidate as an additional sample for the automatic rule generation system to generate another rule to use as the rule.

Abstract: A method of identifying features for a classifier includes identifying a set of elements that share a common characteristic, and then identifying a subset of elements within that set which share a second characteristic. Features are then selected that are more commonly possessed by the elements in the subset than the elements in the set but excluding the subset, and that are more commonly possessed by the elements in the set but excluding the subset, as compared to the elements outside the set. A further method of identifying features for a classifier includes defining a list of features, selecting a first feature from that list, identifying a set of elements that possess that first feature, and then identifying a subset of elements within that set which possess any other feature.

Abstract: A method of identifying features for a classifier includes identifying a set of elements that share a common characteristic, and then identifying a subset of elements within that set which share a second characteristic. Features are then selected that are more commonly possessed by the elements in the subset than the elements in the set but excluding the subset, and that are more commonly possessed by the elements in the set but excluding the subset, as compared to the elements outside the set. A further method of identifying features for a classifier includes defining a list of features, selecting a first feature from that list, identifying a set of elements that possess that first feature, and then identifying a subset of elements within that set which possess any other feature.