ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.
Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, C...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/28/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113630382&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630382 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/28/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630382 113630382 113630382 10.1155/2016/4248026 113630382 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition. aug: au: Akkasi, Abbas Varoğlu, Ekrem Dimililer, Nazife affil: Computer Engineering Department, Eastern Mediterranean University, Famagusta, Northern Cyprus, Mersin 10, Turkey sug: subj: Artificial Intelligence Data Mining Algorithms Software Nomenclature ab: Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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