Corpus annotation for mining biomedical events from literature.
Corpus annotation for mining biomedical events from literature.
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DOI：
10.1186/1471-2105-9-10
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发表时间：
2008-01-08
影响因子：
3
通讯作者：
Tsujii J
中科院分区：
文献类型：
--
作者：
Kim JD;Ohta T;Tsujii J
先进的文本挖掘技术，如论文语义丰富、事件或关系抽取、智能问答等，在生物医学领域日益受到关注。对于这种成功的尝试，从生物学的角度对文本进行注释是不可或缺的。然而，由于任务的复杂性，除了相对简单的术语标注外，语义标注从未大规模尝试过。我们完成了一种新的语义标注-事件标注，它是对Genia语料库中现有标注的补充。语料库已经用词性、句法树、术语等进行了标注。新的标注是在Genia语料库的一半上进行的，包括1000个Medline摘要。它包含了9,372个句子，其中识别了36,114个事件。事件标注过程中的主要挑战是(1)设计满足文本标注特定要求的标注方案，(2)实现反映生物学家对文本的解释的面向生物的标注，(3)确保注释者之间的标注质量的一致性。为了应对这些挑战，我们引入了单面注释和语义类型化等新概念，它们共同为成功完成大规模注释做出了贡献。最终得到的事件标注语料库是类似标注工作中最大、质量最好的语料库之一。我们期待它成为生物医学领域基于NLP(自然语言处理)的TM的宝贵资源。
Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain.
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影响因子：
3
作者：
Chen H;Sharp BM
通讯作者：
Sharp BM
DOI：
10.1049/sb:20045008
发表时间：
2004-06-01
期刊：
Systems Biology
影响因子：
--
作者：
Hucka, M.;Finney, A.;Kitano, H.
通讯作者：
Kitano, H.
影响因子：
3
作者：
Clegg, Andrew B;Shepherd, Adrian J
通讯作者：
Shepherd, Adrian J
影响因子：
5.8
作者：
Daraselia, N;Yuryev, A;Mazo, I
通讯作者：
Mazo, I
影响因子：
14.9
作者：
Bader GD;Cary MP;Sander C
通讯作者：
Sander C