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Synonym set extraction from the biomedical literature by lexical pattern discovery.

McCrae J, Collier N - BMC Bioinformatics (2008)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, 101-8430, Japan. jmccrae@nii.ac.jp

ABSTRACT

Background: 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 1, 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.

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Solution cost versus F-Measure. Chart illustrating increase of F-Measure versus solution cost. The data points are the synset solutions generated by the synset solver before finding the optimal solution and the optimal solution. The chart shows their cost and true F-Measure against the test set.
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Figure 4: Solution cost versus F-Measure. Chart illustrating increase of F-Measure versus solution cost. The data points are the synset solutions generated by the synset solver before finding the optimal solution and the optimal solution. The chart shows their cost and true F-Measure against the test set.

Mentions: In Figure 4 we calculated the experimental results that would be outputted if we stopped the synonym set solver before we had found the optimal solution. This illustrates that the theoretical cost c(...) is correlated to the experimental F-Measure, so better theoretical solutions produce better actual results. Looking at the main results (Table 4) we see the grouping the results in synsets improves the results in terms of totally synonymous results, and although the standard error is large we find the difference is significant at a 99% level using the p-test as described in Yeh [26]. Also the results after synset grouping appeared to be closer, so we analysed the results according to the degree of relation


Synonym set extraction from the biomedical literature by lexical pattern discovery.

McCrae J, Collier N - BMC Bioinformatics (2008)

Solution cost versus F-Measure. Chart illustrating increase of F-Measure versus solution cost. The data points are the synset solutions generated by the synset solver before finding the optimal solution and the optimal solution. The chart shows their cost and true F-Measure against the test set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2335115&req=5

Figure 4: Solution cost versus F-Measure. Chart illustrating increase of F-Measure versus solution cost. The data points are the synset solutions generated by the synset solver before finding the optimal solution and the optimal solution. The chart shows their cost and true F-Measure against the test set.
Mentions: In Figure 4 we calculated the experimental results that would be outputted if we stopped the synonym set solver before we had found the optimal solution. This illustrates that the theoretical cost c(...) is correlated to the experimental F-Measure, so better theoretical solutions produce better actual results. Looking at the main results (Table 4) we see the grouping the results in synsets improves the results in terms of totally synonymous results, and although the standard error is large we find the difference is significant at a 99% level using the p-test as described in Yeh [26]. Also the results after synset grouping appeared to be closer, so we analysed the results according to the degree of relation

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, 101-8430, Japan. jmccrae@nii.ac.jp

ABSTRACT

Background: 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 1, 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.

Show MeSH
Related in: MedlinePlus