A Biological Named Entity Recognizer

Narayanaswamy, Meenakshi; Ravikumar, K. E.; Vijay‐Shanker, K.

doi:10.1142/9789812776303_0040

Cited by 75 publications

(76 citation statements)

References 5 publications

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“…Narayanaswamy, Ravikumar, and Vijay-Shanker proposed a rule-based algorithm for identifying chemical names in text documents [Narayanaswamy et al 2003]. Their paper does not provide the entire set of rules and features on which their system is based, and thus we cannot reproduce their system for a direct comparison.…”

Section: Entity Extractionmentioning

confidence: 99%

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

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

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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.

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Section: Entity Extractionmentioning

confidence: 99%

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

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

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“…Recently, some chemical named entity systems were developed that we describe as follows: ProMiner [7], a dictionary-based system that uses DrugBank for recognition of drug names in MEDLINE; EBIMed uses the drug dictionary from MedlinePlus as source [8]; Narayanaswamy et al [9] describes a system based on a manually developed set of rules that rely heavily upon some crucial lexical information, linguistic constraints of English, and contextual information; Kemp and Lynch [10] proposes a system that identifies chemical names in patent texts with handcrafted rules using dictionaries with chemical name fragments; OSCAR3 [11] relies on an internal lexicon of chemical names and structures initially populated using ChEBI [12]. OSCAR3's performance was evaluated on different corpora with F-score rates between 60-80%.…”

Section: Related Workmentioning

confidence: 99%

Identification of Chemical Entities in Patent Documents

Grego

Pęzik

Couto

et al. 2009

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

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Abstract. Biomedical literature is an important source of information for chemical compounds. However, different representations and nomenclatures for chemical entities exist, which makes the reference of chemical entities ambiguous. Many systems already exist for gene and protein entity recognition, however very few exist for chemical entities. The main reason for this is the lack of corpus to train named entity recognition systems and perform evaluation.In this paper we present a chemical entity recognizer that uses a machine learning approach based on conditional random fields (CRF) and compare the performance with dictionary-based approaches using several terminological resources. For the training and evaluation, a gold standard of manually curated patent documents was used. While the dictionary-based systems perform well in partial identification of chemical entities, the machine learning approach performs better (10% increase in F-score in comparison to the best dictionary-based system) when identifying complete entities.

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“…Fukuda's PROPER system is a representative rulebased system that is freely available for download at (Fukuda, 1998). In addition to , examples of rulebased approaches in the literature include (Narayanaswamy, Ravikumar, & Vijay-Shanker, 2003).…”

Section: Related Workmentioning

confidence: 99%