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Automatically generating hypertext by computing semantic similarity (1997).
| Content Provider | CiteSeerX |
|---|---|
| Author | Green, Stephen J. |
| Abstract | We describe a novel method for automatically generating hypertext links within and between newspaper articles. The method is based on lexical chaining, a technique for extracting the sets of related words that occur in texts. Links between the paragraphs of a single article are built by considering the distribution of the lexical chains in that article. Links between articles are built by considering how the chains in the two articles are related. By using lexical chaining we mitigate the problems of synonymy and polysemy that plague traditional information retrieval approaches to automatic hypertext generation. In order to motivate our research, we discuss the results of a study that shows that humans are inconsistent when assigning hypertext links within newspaper articles. Even if humans were consistent, the time needed to build a large hypertext and the costs associated with the production of such a hypertext make relying on human linkers an untenable decision. Thus we are left to ... |
| File Format | |
| Publisher Date | 1997-01-01 |
| Access Restriction | Open |
| Subject Keyword | Computing Semantic Similarity Hypertext Link Lexical Chaining Newspaper Article Related Word Large Hypertext Lexical Chain Hypertext Make Untenable Decision Single Article Novel Method Automatic Hypertext Generation Human Linkers Plague Traditional Information Retrieval |
| Content Type | Text |
| Resource Type | Article |
