Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach.
Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach.
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DOI：
10.1186/1471-2105-13-s11-s8
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发表时间：
2012-06-26
影响因子：
3
通讯作者：
Warnier P
中科院分区：
文献类型：
--
作者：
Ratkovic Z;Golik W;Warnier P
细菌群落生境涵盖了广泛的不同生境，包括动物和植物宿主，自然，医疗和工业环境。微生物学领域的大量出版物提供了关于细菌生境的最新信息的丰富来源。科学文章中的这些信息是用自然语言表达的，很少以结构化的格式（如数据库）提供。该信息对于基础研究和微生物学应用（例如，医学、农学、食品、生物能源）。从文本中自动提取这些信息将为该领域提供很大的好处。我们提出了一种新的方法提取细菌和它们的位置之间的关系，使用Alvis框架。细菌及其位置的识别是使用基于模式的方法和领域词汇资源实现的。对于环境位置的检测，我们提出了一种新的方法，结合词汇信息和语料库术语的句法语义分析，以克服词汇资源的不完整性。细菌位置关系延伸到句子边界，我们开发了特定领域的规则来处理细菌回指。我们使用Alvis系统参与了BioNLP 2011细菌群落（BB）任务。官方评估结果表明，它达到了最好的性能参与系统。此后的新发展使F分数增加了4.1分。我们已经表明，语义分析和域适应资源的组合是有效的和高效的事件信息提取在细菌生物区域。我们计划采用该方法来处理更大的位置类型集和大规模的科学文章语料库，使微生物学家能够结合实验数据整合和使用提取的知识。
Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field. We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors. We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points. We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.