Abstract:
OBJECTIVE:The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. MATERIALS AND METHODS:This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. RESULTS:We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. CONCLUSION:We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Rinaldi F,Schneider G,Kaljurand K,Hess M,Andronis C,Konstandi O,Persidis Adoi
10.1016/j.artmed.2006.08.005subject
Has Abstractpub_date
2007-02-01 00:00:00pages
127-36issue
2eissn
0933-3657issn
1873-2860pii
S0933-3657(06)00137-0journal_volume
39pub_type
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(00)00050-6
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journal_title:Artificial intelligence in medicine
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2015.10.003
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2013.02.003
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journal_title:Artificial intelligence in medicine
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doi:10.1016/j.artmed.2020.101852
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abstract:OBJECTIVE:We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers. This is an essential process, useful in it...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2011.04.011
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(00)00112-3
更新日期:2001-06-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101804
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.04.001
更新日期:2005-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2009.07.012
更新日期:2010-02-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2011.06.008
更新日期:2011-09-01 00:00:00
abstract:OBJECTIVE:The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS:All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored b...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2007.04.003
更新日期:2007-07-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.07.004
更新日期:2005-06-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(02)00076-3
更新日期:2003-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2014.07.003
更新日期:2014-10-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.01.009
更新日期:2004-06-01 00:00:00
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journal_title:Artificial intelligence in medicine
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doi:10.1016/j.artmed.2020.101950
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(97)00046-8
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journal_title:Artificial intelligence in medicine
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2011.08.005
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(98)00040-2
更新日期:1998-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.11.001
更新日期:2006-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101881
更新日期:2020-07-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.10.002
更新日期:2016-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章,评审
doi:10.1016/s0933-3657(01)00087-2
更新日期:2001-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2018.06.004
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