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Cui, H.; Heidorn, P.B.; Zhang, H.: ¬An approach to automatic classification of text for information retrieval (2002)
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- Abstract
- In this paper, we explore an approach to make better use of semi-structured documents in information retrieval in the domain of biology. Using machine learning techniques, we make those inherent structures explicit by XML markups. This marking up has great potentials in improving task performance in specimen identification and the usability of online flora and fauna.
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Cui, H.; Heidorn, P.B.: ¬The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions (2007)
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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.
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Cui, H.: CharaParser for fine-grained semantic annotation of organism morphological descriptions (2012)
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- Abstract
- Biodiversity information organization is looking beyond the traditional document-level metadata approach and has started to look into factual content in textual documents to support more intelligent and semantic-based access. This article reports the development and evaluation of CharaParser, a software application for semantic annotation of morphological descriptions. CharaParser annotates semistructured morphological descriptions in such a detailed manner that all stated morphological characters of an organ are marked up in Extensible Markup Language format. Using an unsupervised machine learning algorithm and a general purpose syntactic parser as its key annotation tools, CharaParser requires minimal additional knowledge engineering work and seems to perform well across different description collections and/or taxon groups. The system has been formally evaluated on over 1,000 sentences randomly selected from Volume 19 of Flora of North American and Part H of Treatise on Invertebrate Paleontology. CharaParser reaches and exceeds 90% in sentence-wise recall and precision, exceeding other similar systems reported in the literature. It also significantly outperforms a heuristic rule-based system we developed earlier. Early evidence that enriching the lexicon of a syntactic parser with domain terms alone may be sufficient to adapt the parser for the biodiversity domain is also observed and may have significant implications.
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Cui, H.: Competency evaluation of plant character ontologies against domain literature (2010)
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- Date
- 1. 6.2010 9:55:22
