A method and computer program product and tool for increasing efficiency of an intelligent horizon scanning process. The horizon scanning process methodology uses a set of negative training examples, a universum data set of articles, and a data subset of unlabeled instances from received positive class and unlabeled electronic documents. Further a ranking model that can use partial pairwise preferences is implemented to generate a list of recommended articles for output to a user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for intelligent horizon scanning comprising: accessing web-based electronic documents, said documents including positive class and unlabeled electronic documents; generating a training dataset of a negative class from said positive class documents; generating a universum dataset; and generating an unlabeled data subset; classifying positive articles based on said training dataset, universum dataset and unlabeled data subset, and ranking said classified positive documents articles, wherein a programmed hardware processor device performs said accessing, said training dataset, universum dataset and unlabeled data subset generating, classifying and ranking steps. 2. The method of claim 1, where the generating a negative class training dataset for training based on the positive class documents comprises: hierarchically clustering the positive labeled and unlabeled dataset to form a dendrogram data structure; identifying from the dendrogram data structure all the positive samples belonging to a same positive cluster; identifying representative words from the positive cluster; identifying documents from clusters other than the positive cluster such that they do not contain any of the representative words identified, wherein a document set identified from clusters other than the positive cluster provides said negative class training sample. 3. The method of claim 2, where the generating a universum dataset from said positive class comprises: identifying a top K clusters in descending order of their distance from the positive cluster; identifying the data source sections corresponding to documents from the top K clusters, said documents having said identified data source sections labeled as documents S; filtering out sections from S which publish any positive class documents; and providing all the documents published at S as said universum. 4. The method of claim 2, wherein the generating an unlabeled dataset from said positive class comprises: identifying top K features from the dataset which correlate with the positive class, selecting said identified top K features; constructing a decision tree structure from the dataset after said selecting said top k features; identifying branches of said decision tree corresponding to leaves with a pure or majority positive class, said branches becoming rules for selecting samples from said data set of unlabeled data. 5. A tool for intelligent horizon scanning comprising: a memory storage device; a programmed hardware processor device coupled with said memory, said hardware processor device configured for: accessing web-based electronic documents, said documents including positive class and unlabeled electronic documents; generating a training dataset of a negative class from said positive class documents; generating a universum dataset; and generating an unlabeled data subset; classifying positive articles based on said training dataset, universum dataset and unlabeled data subset, and ranking said classified positive documents articles. 6. The tool of claim 5, where the creating a negative class training sample for training based on a positive class documents comprises: hierarchically clustering the positive labeled and unlabeled dataset to form a dendrogram data structure; identifying from the dendrogram data structure all the positive samples belonging to a same positive cluster; identifying representative words from the positive cluster; identifying documents from clusters other than the positive cluster such that they do not contain any of the representative words identified, wherein a document set identified from clusters other than the positive cluster provides said negative class training sample. 7. The tool of claim 6, where the generating a universum dataset from said positive class comprises: identifying a top K clusters in descending order of their distance from the positive cluster; identifying the data source sections corresponding to documents from the top K clusters, said documents having said identified data source sections labeled as documents S; filtering out sections from S which publish any positive class documents; and providing all the documents published at S as said universum. 8. The tool of claim 7, wherein the generating an unlabeled dataset from said positive class comprises: identifying top K features from the dataset which correlate with the positive class, selecting said identified top K features; constructing a decision tree structure from the dataset after said selecting said top k features; identifying branches of said decision tree corresponding to leaves with a pure or majority positive class, said branches becoming rules for selecting samples from said data set of unlabeled data. 9. A computer program product for intelligent horizon scanning, the computer program product comprising a computer readable storage medium, the computer readable storage medium excluding a propagating signal, the computer readable storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method comprising: accessing web-based electronic documents, said documents including positive class and unlabeled electronic documents; generating a training dataset of a negative class from said positive class documents; generating a universum dataset; and generating an unlabeled data subset; classifying positive articles based on said training dataset, universum dataset and unlabeled data subset, and ranking said classified positive documents articles. 10. The computer program product as claimed in claim 9, where the generating a negative class training dataset for training based on the positive class documents comprises: hierarchically clustering the positive labeled and unlabeled dataset to form a dendrogram data structure; identifying from the dendrogram data structure all the positive samples belonging to a same positive cluster; identifying representative words from the positive cluster; identifying documents from clusters other than the positive cluster such that they do not contain any of the representative words identified, wherein a document set identified from clusters other than the positive cluster provides said negative class training sample. 11. The computer program product as claimed in claim 10, where the generating a universum dataset from said positive class comprises: identifying a top K clusters in descending order of their distance from the positive cluster; identifying the data source sections corresponding to documents from the top K clusters, said documents having said identified data source sections labeled as documents S; filtering out sections from S which publish any positive class documents; and providing all the documents published at S as said universum. 12. The computer program product as claimed in claim 11, wherein the generating an unlabeled dataset from said positive class comprises: identifying top K features from the dataset which correlate with the positive class, selecting said identified top K features; constructing a decision tree structure from the dataset after said selecting said top k features; identifying branches of said decision tree corresponding to leaves with a pure or majority positive class, said branches becoming rules for selecting samples from said data set of unlabeled data.