Volume 7 Supplement 1

Text mining for chemistry and the CHEMDNER track

Research

Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. Articles have undergone the journal's standard peer review process.

  1. Research

    CHEMDNER: The drugs and chemical names extraction challenge

    Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as...

    Martin Krallinger, Florian Leitner, Obdulia Rabal, Miguel Vazquez, Julen Oyarzabal and Alfonso Valencia

    Journal of Cheminformatics 2015 7(Suppl 1):S1

    Published on: 19 January 2015

  2. Research

    The CHEMDNER corpus of chemicals and drugs and its annotation principles

    The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the a...

    Martin Krallinger, Obdulia Rabal, Florian Leitner, Miguel Vazquez, David Salgado, Zhiyong Lu, Robert Leaman, Yanan Lu, Donghong Ji, Daniel M Lowe, Roger A Sayle, Riza Theresa Batista-Navarro, Rafal Rak, Torsten Huber, Tim Rocktäschel, Sérgio Matos…

    Journal of Cheminformatics 2015 7(Suppl 1):S2

    Published on: 19 January 2015

  3. Research

    A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

    Chemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number ...

    Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang and Hua Xu

    Journal of Cheminformatics 2015 7(Suppl 1):S8

    Published on: 19 January 2015

  4. Research

    Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations

    Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system perf...

    Tsendsuren Munkhdalai, Meijing Li, Khuyagbaatar Batsuren, Hyeon Ah Park, Nak Hyeon Choi and Keun Ho Ryu

    Journal of Cheminformatics 2015 7(Suppl 1):S9

    Published on: 19 January 2015

  5. Research

    Chemical entity extraction using CRF and an ensemble of extractors

    As we are witnessing a great interest in identifying and extracting chemical entities in academic articles, many approaches have been proposed to solve this problem. In this work we describe a probabilistic fr...

    Madian Khabsa and C Lee Giles

    Journal of Cheminformatics 2015 7(Suppl 1):S12

    Published on: 19 January 2015

  6. Research

    Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization

    The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of rese...

    Hong-Jie Dai, Po-Ting Lai, Yung-Chun Chang and Richard Tzong-Han Tsai

    Journal of Cheminformatics 2015 7(Suppl 1):S14

    Published on: 19 January 2015

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