Abstract: A method of automatically constructing a model of an activity from an unsupervised examination of a plurality of textual documents describing the activity is comprised of: extracting prototypical steps from the plurality of textual documents; sequencing the extracted steps; aligning the sequenced steps; and constructing the model based on the aligned steps. The model may take the form of a step vs. position matrix which identifies the prototypical steps that make up the activity and provides the probability of each step occupying each position within the activity. The model thus constitutes common sense knowledge that encodes the stereotypical steps of an activity and the stereotypical sequencing of the steps.

Abstract: One example of a semantically informed text operation comprises selecting a source region of a document and determining if the source region has a surface region bi-directionally coupled to a semantic object. The coupled semantic object is identified as are the presentation(s) associated with the semantic object. A target region of the same or anther document is selected. Any of the presentations that cannot be expressed in the target region are eliminated to identify a set of remaining presentations. A set of semantic choices based on the remaining presentations is determined. One of the semantic choices is selected and executed in the target region.

Abstract: A method and apparatus for performing an informed semantic merge operation comprises selecting a source region in a document and a target region in the same or a different document. A bi-directionally coupled surface region is identified in the source region and a bi-directionally coupled surface region is identified in the target region. A first semantic object coupled to the surface region in the source region is identified and a second semantic object coupled to the surface region in the target region is identified. The subcomponents of the first semantic object are combined with the subcomponents of the second semantic object by merging.

Abstract: A method of generating an ideographic representation of a name given in a letter based system begins with a determination of the language of original. After determining the language of origin for the name, the name is segmented into a segmentation sequence in response to the determined language of origin. A candidate representation is generated for the segmentation sequence based on ideographic representations of the segments. A corpus is used to validate the candidate representation. The corpus can be either a monolingual corpus or a multilingual corpus. The method can also include adding an additional validation step using either a monolingual corpus or a multilingual corpus, which ever was not used in the first validation step. Because of the rules governing abstracts, this abstract should not be used to construe the claims.

Abstract: A method of automatically categorizing terms extracted from a text corpus is comprised of identifying lexical atoms in a text corpus as terms. The identified terms are extracted based on a relation that exists between the terms. A weight is assigned to each relation. A graphical representation of the relationships among terms is constructed by using terms as vertices and relations as weighted links between the vertices. A vertex score is calculated for each of the vertices of the graph. Each term is categorized based on its vertex score. The graphical representation may be revised based on its structure and/or the calculated vertex scores. Because of the rules governing abstracts, this abstract should not be used to construe the claims.