NetiNeti: discovery of scientific names from text using machine learning methods
Identifieur interne : 000252 ( Main/Exploration ); précédent : 000251; suivant : 000253NetiNeti: discovery of scientific names from text using machine learning methods
Auteurs : Lakshmi Manohar Akella [États-Unis] ; Catherine N. Norton [États-Unis] ; Holly Miller [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2012.
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Abstract
A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.
We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3%) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central’s full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages.
We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at
Url:
DOI: 10.1186/1471-2105-13-211
PubMed: 22913485
PubMed Central: 3542245
Affiliations:
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Norton</name><affiliation wicri:level="2"><nlm:aff id="I1">MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Miller, Holly" sort="Miller, Holly" uniqKey="Miller H" first="Holly" last="Miller">Holly Miller</name><affiliation wicri:level="2"><nlm:aff id="I1">MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Artificial Intelligence</term><term>Classification</term><term>Data Mining</term><term>MEDLINE</term><term>PubMed</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Artificial Intelligence</term><term>Classification</term><term>Data Mining</term><term>MEDLINE</term><term>PubMed</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.</p></sec><sec><title>Results</title><p>We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3%) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central’s full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages.</p></sec><sec><title>Conclusions</title><p>We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at
<ext-link ext-link-type="uri" xlink:href="http://namefinding.ubio.org.">http://namefinding.ubio.org.</ext-link></p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Poon, H" uniqKey="Poon H">H Poon</name></author><author><name sortKey="Vanderwende, L" uniqKey="Vanderwende L">L Vanderwende</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gerner, M" uniqKey="Gerner M">M Gerner</name></author><author><name sortKey="Nenadic, G" uniqKey="Nenadic G">G Nenadic</name></author><author><name sortKey="Bergman, Cm" uniqKey="Bergman C">CM Bergman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kappeler, T" uniqKey="Kappeler T">T Kappeler</name></author><author><name sortKey="Kaljurand, K" uniqKey="Kaljurand K">K Kaljurand</name></author><author><name sortKey="Rinaldi, F" uniqKey="Rinaldi F">F 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Akella</name></region><name sortKey="Akella, Lakshmi Manohar" sort="Akella, Lakshmi Manohar" uniqKey="Akella L" first="Lakshmi Manohar" last="Akella">Lakshmi Manohar Akella</name><name sortKey="Miller, Holly" sort="Miller, Holly" uniqKey="Miller H" first="Holly" last="Miller">Holly Miller</name><name sortKey="Norton, Catherine N" sort="Norton, Catherine N" uniqKey="Norton C" first="Catherine N" last="Norton">Catherine N. 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