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 availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus

Differential evolution-based feature selection technique for anaphora resolution

In this paper a differential evolution (DE)-based feature selection technique is developed for anaphora resolution in a resource-poor language, namely Bengali. We discuss the issues of adapting a state-of-the-art English anaphora resolution system for a resource-poor language like Bengali. Performance of any anaphoric resolver greatly depends on the quality of a high accurate mention detector and the use of appropriate features for anaphora resolution. We develop a number of models for mention detection based on machine learning and heuristics. In anaphora resolution there is no globally accepted metric for measuring the performance, and each of them such as MUC, B3 , CEAF, Blanc exhibit significantly different behaviors. Our proposed feature selection technique determines the near-optimal feature set by optimizing each of these evaluation metrics. Experiments show how a language-dependent system (designed primarily for English) can attain reasonably good performance level when re-trained and tested on a new language with a proper subset of features. Evaluation results yield the F-measure values of 66.70, 59.47, 51.56, 33.08 and 72.75 % for MUC, B 3, CEAFM, CEAFE and BLANC, respectivel

Proceedings of Intelligent Computing and Technologies Conference

This proceeding contains articles on the various research ideas of the academic community and practitioners presented at the Intelligent Computing and Technologies Conference (ICTCon2021). ICTCon2021 was jointly organized by Assam Science and Technology University (ASTU), and Central Institute of Technology Kokrajhar (CITK) on March 15th–16th, 2021. Conference Title: Intelligent Computing and Technologies ConferenceConference Acronym: ICTCon2021Conference Date: 15–16 March 2021Conference Location: Online (Virtual Mode)Conference Organizers: Assam Science and Technology University (ASTU) and Central Institute of Technology Kokrajhar (CITK)

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 availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus