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Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents.

机译:词汇句法模式匹配对本体丰富临床文件的有效性。

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OBJECTIVE: To evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents. METHODS: Two domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains. RESULTS: For NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%. CONCLUSION: The LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.
机译:目的:使用临床文献评估词汇-句法模式(LSP)匹配方法对本体丰富化的有效性。方法:使用相同的方法分别研究了两个领域。我们使用放射学文献来丰富RadLex,并使用病理学文献来丰富美国国家癌症研究所词库(NCIT)。一些已知的LSP被用于语义知识提取。我们首先检索了跨越两个大型临床资料库的所有包含LSP的句子,并检查了LSP的频率。从这个集合中,我们随机抽样了LSP实例,这些实例已由人类法官检查过。我们使用了两步法来确定这些模式用于富集的效用。第一步,领域专家从LSP中的每个句子注释医学上有意义的词(MMT)。第二步,RadLex和NCIT策展人评估了可以向资源中添加多少MMT。为了量化此LSP方法的效用,我们定义了两个评估指标:建议率(SR)和接受率(AR)。我们使用这些度量来估计两个域中每个域的概念和关系的收益。结果:对于NCIT,概念SR为24%,关系SR为65%。概念AR为21%,关系AR为14%。对于RadLex,概念SR为37%,关系SR为55%。概念AR为11%,关系AR为44%。结论:LSP匹配方法是一种在生物医学领域发现概念和概念关系的有效方法。

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