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  • × author_ss:"Cui, H."
  1. Cui, H.; Heidorn, P.B.: ¬The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions (2007) 0.01 
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    Abstract
    To automatically convert legacy data of taxonomic descriptions into extensible markup language (XML) format, the authors designed a machine-learning-based approach. In this project three corpora of taxonomic descriptions were selected to prove the hypothesis that domain knowledge and conventions automatically induced from some semistructured corpora (i.e., base corpora) are useful to improve the markup performance of other less-structured, quite different corpora (i.e., evaluation corpora). The "structuredness" of the three corpora was carefully measured. Basing on the structuredness measures, two of the corpora were used as the base corpora and one as the evaluation corpus. Three series of experiments were carried out with the MARTT (markuper of taxonomic treatments) system the authors developed to evaluate the effectiveness of different methods of using the n-gram semantic class association rules, the element relative position probabilities, and a combination of the two types of knowledge mined from the automatically marked-up base corpora. The experimental results showed that the induced knowledge from the base corpora was more reliable than that learned from the training examples alone, and that the n-gram semantic class association rules were effective in improving the markup performance, especially on the elements with sparse training examples. The authors also identify a number of challenges for any automatic markup system using taxonomic descriptions.
  2. Cui, H.: Competency evaluation of plant character ontologies against domain literature (2010) 0.01 
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    Date
    1. 6.2010 9:55:22
  3. Cui, H.; Boufford, D.; Selden, P.: Semantic annotation of biosystematics literature without training examples (2010) 0.00 
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    Abstract
    This article presents an unsupervised algorithm for semantic annotation of morphological descriptions of whole organisms. The algorithm is able to annotate plain text descriptions with high accuracy at the clause level by exploiting the corpus itself. In other words, the algorithm does not need lexicons, syntactic parsers, training examples, or annotation templates. The evaluation on two real-life description collections in botany and paleontology shows that the algorithm has the following desirable features: (a) reduces/eliminates manual labor required to compile dictionaries and prepare source documents; (b) improves annotation coverage: the algorithm annotates what appears in documents and is not limited by predefined and often incomplete templates; (c) learns clean and reusable concepts: the algorithm learns organ names and character states that can be used to construct reusable domain lexicons, as opposed to collection-dependent patterns whose applicability is often limited to a particular collection; (d) insensitive to collection size; and (e) runs in linear time with respect to the number of clauses to be annotated.

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