High-Throughput Identification of Chemistry in Life Science Texts

Corbett, Peter; Murray‐Rust, Peter

doi:10.1007/11875741_11

Cited by 75 publications

(80 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our experiments show that our chemical formula and name search engine outperforms general purpose search engines (as expected) and provides better results than Oscar3, [Corbett and Murray-Rust 2006] (version Alpha1, the latest version available at the time this work was done) or a rule-based search engine on our dataset. However, as the Netflix Prize 3 , the KDD Cup 2009 4 challenges, and other research projects (for a tutorial see [Polikar 2006]) have shown, to obtain the best performance in any real-life annotation or classification task, one has to employ an ensemble of different classifiers.…”

Section: Introductionsupporting

confidence: 73%

“…Corbett and Murray-Rust proposed Oscar3, an improvement on Oscar2 [Corbett and Murray-Rust 2006]. Besides its other features, Oscar3 provides chemical entity tagging.…”

Section: Entity Extractionmentioning

confidence: 99%

“…Since the work was done at CambridgeSoft, his software is also not available freely. Corbett and Murray-Rust have proposed the OPSIN sub-system as part of the OSCAR3 system [Corbett and Murray-Rust 2006]. OPSIN uses a finite-state grammar, which is "less expressive but more tractable" than the context-free grammars used in previous methods along with a set of informal rules.…”

Section: Chemical Name Segmentationmentioning

confidence: 99%

“…Such segmentation and indexing allow end-users to perform partial name searches. Typically, tools, like Name=Struct [Brecher 1999], CHEMorph [Kremer et al 2006], and OPSIN [Corbett and Murray-Rust 2006], segment a chemical name into its morphemes map the morphemes into their chemical structures, and use these structures to construct the structure of the named chemical. We believe that our segmentation algorithm can be used by name-to-structure generating software for segmenting a chemical name to its constituent morphemes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

End users use chemical search engines to search for chemical formulae and chemical names. Chemical search engines identify and index chemical formulae and chemical names appearing in text documents to support efficient search and retrieval in the future. Identifying chemical formulae and chemical names in text automatically has been a hard problem that has met with varying degrees of success in the past. We propose algorithms for chemical formula and chemical name tagging using Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) that achieve higher accuracy than existing (published) methods. After chemical entities have been identified in text documents, they must be indexed. In order to support user-provided search queries that require a partial match between the chemical name segment used as a keyword or a partial chemical formula, all possible (or a significant number of) sub-formulae of formulae that appear in any document and all possible sub-terms (e.g., 'methyl') of chemical names (e.g., 'methylethyl ketone') must be indexed. Indexing all possible sub-formulae and sub-terms results in an exponential increase in the storage and memory requirements, and the time taken to process the indices. We propose techniques to prune the indices significantly without reducing the quality of the returned results significantly. Finally, we propose multiple query semantics to allow users to pose different types of partial search queries for chemical entities. We demonstrate empirically that our search engines improve the relevance of the returned results for search queries involving chemical entities.

show abstract

Section: Introductionsupporting

confidence: 73%

“…Corbett and Murray-Rust proposed Oscar3, an improvement on Oscar2 [Corbett and Murray-Rust 2006]. Besides its other features, Oscar3 provides chemical entity tagging.…”

Section: Entity Extractionmentioning

confidence: 99%

Section: Chemical Name Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

show abstract

“…Therefore, the first necessary step is to extract all chemical terms from each document of the repository. We decided to use the OSCAR3 framework [4] which is currently the only open source project on the market focusing on the automatic extraction of chemical entities. It offers a wide range of functionalities for annotating chemical entities, reactions and concepts.…”

Section: Workflowmentioning

confidence: 99%

Using Wikipedia categories for compact representations of chemical documents

Köhncke

Balke

2010

Proceedings of the 19th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Today, Web pages are usually accessed using text search engines, whereas documents stored in the deep Web are accessed through domain-specific Web portals. These portals rely on external knowledge bases, respectively ontologies, mapping documents to more general concepts allowing for suitable classifications and navigational browsing. Since automatically generated ontologies are still not satisfactory for advanced information retrieval tasks, most portals heavily rely on hand-crafted domain-specific ontologies. This, however, also leads to high creation and maintaining costs. On the other hand, a freely available community maintained, if somewhat general, knowledge base is offered by Wikipedia. During the last years the coverage of Wikipedia has reached a large pool of information including articles from almost all domains. In this paper, we investigate the use of Wikipedia categories to describe the content of chemical documents in a compact form. We compare the results to the domain-specific ChEBI ontology and the results show that Wikipedia categories indeed allow useful descriptions for chemical documents that are even better than descriptions from the ChEBI ontology.

show abstract