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Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text French, Leon; Lane, Suzanne; Xu, Lydia; Siu, Celia; Kwok, Cathy; Chen, Yigi; Krebs, Claudia; Pavlidis, Paul
Description
Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.
Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.
Item Metadata
| Title |
Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text
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| Creator | |
| Contributor | |
| Date Created |
2012
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| Date Issued |
2019-03-11
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| Description |
Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.
Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.
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| Subject | |
| Type | |
| Notes | |
| Date Available |
2019-03-11
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| Provider |
University of British Columbia Library
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| License |
CC0 Waiver
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| DOI |
10.14288/1.0363992
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| URI | |
| Publisher DOI | |
| Rights URI | |
| Aggregated Source Repository |
Dataverse
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Item Media
Item Citations and Data
Licence
CC0 Waiver