Organizing fragments of meaningful text
Organizing and/or aligning fragments of text that are included in a set of physical and/or digital documents so that the arrangement of the text fragments is in a readily understandable and meaningful format for a given reader. This organization and/or alignment uses a relation model of the various text fragments to correlate a meaning between and amongst the various text fragments to ultimately determine the final alignment and/or arrangement of those text fragments.
1. A computer-implemented method (CIM) comprising:
receive, from a corpus of text documents, a plurality of text fragments, with the plurality of text fragments being text strings that are organized in a staggered manner;
identify, from the plurality of text fragments, a first arrangement of the plurality of text fragments;
responsive to the identification of the first arrangement, extracting a first set of text fragments to obtain an extracted set of text fragments;
generating a relation model based upon the extracted set of text fragments, with the relation model including information indicative of a semantic relatedness value defining the extracted set of text fragments;
clustering the extracted set of text fragments into a first group, based, at least in part, upon the relation model;
combining the clustered set of text fragments from the first group into composite text objects, with the composite text objects including a vector representing a function of the extracted set of text fragments; and
optimizing an alignment of the composite text objects to obtain an organized text fragment.
2. The CIM of claim 1 wherein the semantic relatedness value defining the extracted set of text fragments is determined from a cosine similarity of the extracted set of text fragments.
3. The CIM of claim 1 wherein a vector for the composite text objects is a function of the clustered set of text fragments.
4. The CIM of claim 1 wherein the semantic relatedness value for the composite text objects is determined through the use of a semantic web graph.
5. The CIM of claim 1 wherein the combination of the clustered set of text fragments from the first group into composite text objects includes determining a physical proximity between multiple text fragments within the clustered set of text fragments from the first group.
6. The CIM of claim 1 wherein the combination of the clustered set of text fragments from the first group is done by considering the vertical or horizontal alignment of the text fragments within the clustered set of text fragments from the first group.
7. A computer program product (CPP) comprising:
a machine-readable storage device; and
computer code stored on the machine readable storage device, with the computer code including instructions and data for causing a processor set to perform operations including the following:
receive, from a corpus of text documents, a plurality of text fragments, with the plurality of text fragments being text strings that are organized in a staggered manner,
identify, from the plurality of text fragments, a first arrangement of the plurality of text fragments,
responsive to the identification of the first arrangement, extracting a first set of text fragments to obtain an extracted set of text fragments,
generating a relation model based upon the extracted set of text fragments, with the relation model including information indicative of a semantic relatedness value defining the extracted set of text fragments,
clustering the extracted set of text fragments into a first group, based, at least in part, upon the relation model,
combining the clustered set of text fragments from the first group into composite text objects, with the composite text objects including a vector representing a function of the extracted set of text fragments, and
optimizing an alignment of the composite text objects to obtain an organized text fragment.
8. The CPP of claim 7 wherein the semantic relatedness value defining the extracted set of text fragments is determined from a cosine similarity of the extracted set of text fragments.
9. The CPP of claim 7 wherein a vector for the composite text objects is a function of the clustered set of text fragments.
10. The CPP of claim 7 wherein the semantic relatedness value for the composite text objects is determined through the use of a semantic web graph.
11. The CPP of claim 7 wherein the combination of the clustered set of text fragments from the first group into composite text objects includes determining a physical proximity between multiple text fragments within the clustered set of text fragments from the first group.
12. The CPP of claim 7 wherein the combination of the clustered set of text fragments from the first group is done by considering the vertical or horizontal alignment of the text fragments within the clustered set of text fragments from the first group.
13. A computer system (CS) comprising:
a processor(s) set;
a machine-readable storage device; and
computer code stored on the machine readable storage device, with the computer code including instructions and data for causing the processor set to perform operations including the following:
receive, from a corpus of text documents, a plurality of text fragments, with the plurality of text fragments being text strings that are organized in a staggered manner,
identify, from the plurality of text fragments, a first arrangement of the plurality of text fragments,
responsive to the identification of the first arrangement, extracting a first set of text fragments to obtain an extracted set of text fragments,
generating a relation model based upon the extracted set of text fragments, with the relation model including information indicative of a semantic relatedness value defining the extracted set of text fragments,
clustering the extracted set of text fragments into a first group, based, at least in part, upon the relation model,
combining the clustered set of text fragments from the first group into composite text objects, with the composite text objects including a vector representing a function of the extracted set of text fragments, and
optimizing an alignment of the composite text objects to obtain an organized text fragment.
14. The CS of claim 13 wherein the semantic relatedness value defining the extracted set of text fragments is determined from a cosine similarity of the extracted set of text fragments.
15. The CS of claim 13 wherein a vector for the composite text objects is a function of the clustered set of text fragments.
16. The CS of claim 13 wherein the semantic relatedness value for the composite text objects is determined through the use of a semantic web graph.
17. The CS of claim 13 wherein the combination of the clustered set of text fragments from the first group into composite text objects includes determining a physical proximity between multiple text fragments within the clustered set of text fragments from the first group.
18. The CS of claim 13 wherein the combination of the clustered set of text fragments from the first group is done by considering the vertical or horizontal alignment of the text fragments within the clustered set of text fragments from the first group.