Difference between revisions of "Synonym set extraction from the biomedical literature by lexical pattern discovery"
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|Synonym set extraction from the biomedical literature by lexical pattern discovery|
|Authors:||John McCrae, Nigel Collier|
|Citation:||BMC bioinformatics 9 (1): 159. 2008.|
|Publication type:||Journal article|
|Google Scholar cites:||Citations|
|Added by Wikilit team:||Added on initial load|
|Article:||Google Scholar BASE PubMed|
|Other scholarly wikis:||AcaWiki Brede Wiki WikiPapers|
|Web search:||Bing Google Yahoo! — Google PDF|
Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst , but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. Results
We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. Conclusion
We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.
"We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous."
|Domains:||Health, Information science|
|Theory type:||Design and action|
|Research design:||Mathematical modeling|
|Data source:||Wikipedia pages|
|Collected data time dimension:||Cross-sectional|
|Unit of analysis:||Website|
|Wikipedia data extraction:||Live Wikipedia|
|Wikipedia page type:||Article|
|Wikipedia language:||Not specified|
"We conclude that for domains with a large amount of specific vocabulary most of the resources we studied perform worse than the automatic method we have developed here. Also given the amount of effort required to manually construct a resource, automatic thesaurus construction may prove more useful in many situations, either to aid construction or in replacement of manual construction. More importantly we have shown that we can easily automatically find patterns and we do not require any prior knowledge of the language's grammar in order to do this. Even though the patterns we generated were weak by themselves we showed that by statistically combining them we can get a much stronger result. We have also shown that we do not need to know a large number of synsets to develop an accurate classifier; this implies most importantly that this method can be used quickly on a different language. We tested our method on only a limited domain but we feel it would likely generalize well to other domains. Our novel synset grouping method not only converted the result to something more applicable, but also improved on the results for both a strict definition of synonymy, and a more relaxed definition."
"We conclude that for domains with a large amount of specific vocabulary most of the resources we studied perform worse than the automatic method we have developed here."