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Your search keyword '"Chemical named entity recognition"' showing total 5 results
Start Over Descriptor "Chemical named entity recognition" Topic text mining
5 results on '"Chemical named entity recognition"'

1. Automatic Recognition of Chemical Entity Mentions in Texts of Scientific Publications.

Author

Biziukova, N. Yu., Tarasova, O. A., Rudik, A. V., Filimonov, D. A., and Poroikov, V. V.

Abstract

The huge space of experimental data on biological and chemical objects and their interactions contributes to the rapid growth of scientific publications containing their analysis. Such data may include characteristics of low-molecular-weight compounds, results of their biological activity evaluation, and their interaction with human and animal proteins, methods of synthesis of organic compounds, and their classification. The past decades saw the development of methods for automated extraction of data from texts of scientific publications, including those for retrieval of names of organic compounds. These data can be used for the automatic identification of the names of organic compounds, including all possible synonyms. Since the topics of scientific publications are diverse, the extracted data can be applied to obtain information about (1) classification of organic compounds (2) methods of synthesis of a given organic compound; (3) physicochemical properties of this compound; (4) its interaction with high-molecular-weight compounds (including proteins, mRNA of animals and humans); and (5) the therapeutic properties of organic compounds, the active substance of the drug, and data on clinical trials. This review considers the methods aimed at searching and extracting data on names of low-molecular-weight compounds and interactions between them and animal and human proteins (biological objects), as well as data on experimentally confirmed biological activity and the effects of organic compounds (including drugs) on pathological processes. Here, we discuss the methods developed and the results of their application published over the past 10 years. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Identification of Chemical Entities in Patent Documents

Author

Grego, Tiago, Pęzik, Piotr, Couto, Francisco M., Rebholz-Schuhmann, Dietrich, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Omatu, Sigeru, editor, Rocha, Miguel P., editor, Bravo, José, editor, Fernández, Florentino, editor, Corchado, Emilio, editor, Bustillo, Andrés, editor, and Corchado, Juan M., editor

Published
2009
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3. Recognizing chemicals in patents: a comparative analysis.

Author

Habibi, Maryam, Wiegandt, David, Schmedding, Florian, and Leser, Ulf

Subjects
*CHEMICALS, *PATENTS, *PHARMACEUTICAL industry, *SCIENTIFIC community, *TEXT mining, *DATA mining, *COMPARATIVE studies
Abstract

Recently, methods for Chemical Named Entity Recognition (NER) have gained substantial interest, driven by the need for automatically analyzing todays ever growing collections of biomedical text. Chemical NER for patents is particularly essential due to the high economic importance of pharmaceutical findings. However, NER on patents has essentially been neglected by the research community for long, mostly because of the lack of enough annotated corpora. A recent international competition specifically targeted this task, but evaluated tools only on gold standard patent abstracts instead of full patents; furthermore, results from such competitions are often difficult to extrapolate to real-life settings due to the relatively high homogeneity of training and test data. Here, we evaluate the two state-of-the-art chemical NER tools, tmChem and ChemSpot, on four different annotated patent corpora, two of which consist of full texts. We study the overall performance of the tools, compare their results at the instance level, report on high-recall and high-precision ensembles, and perform cross-corpus and intra-corpus evaluations. Our findings indicate that full patents are considerably harder to analyze than patent abstracts and clearly confirm the common wisdom that using the same text genre (patent vs. scientific) and text type (abstract vs. full text) for training and testing is a pre-requisite for achieving high quality text mining results. [ABSTRACT FROM AUTHOR]

Published
2016
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5. A Comparative Analysis of Chemical Named Entity Recognition Using Support Vector Machines

Author

Azari, Samaneh and Varoğlu, Ekrem

Subjects
Chemical Named Entity Recognition, Text Mining, Support Vector Machines, Cheminformatics, Drugs - Research - Data processing, Computer Engineering, Wrapper Based Feature Subset Selection, Feature Extraction
Abstract

Cheminformatics is the synthesis of computer science and chemistry to collect knowledge about chemicals to provide useful information for drug development. Chemical named entity recognition (CHEM-NER) is the crucial first step to extract useful information from chemical publications and patents. In this dissertation, a classification system based on support vector machine (SVM) which uses wrapper based feature subset selection algorithms is proposed for the CHEM-NER task. The SVM classifier for recognizing chemical named entities needs training and evaluation corpora. Three different standard chemical corpora which contain different number of classes have been used to address the binary-class and multi-class classification problems. Wrapper based feature subset selection algorithms such as Forward Selection, Backward Selection and Simplified Forward Search are used in an attempt to find the most relevant subset of features among several features. The features used include several variations of morphological features, lexical features, orthographic features and spaces. The aim of these experiments is to investigate the classification performance using different subsets of features as well as discovering the most relevant corpus among the available corpora for CHEM-NER task. The results show that in general Forward Search algorithm is more successful in selecting the most suitable subset of features for the CHEM-NER task in terms of F-score measure. Keywords: Chemical Named Entity Recognition, Feature Extraction, Wrapper Based Feature Subset Selection, Support Vector Machines, Text Mining. ÖZ: Kemoinformatik, ilaç yapımında kullanılmak üzere kimyasallar hakkında gerekli bilgiyi elde etmek için bilgisayar bilimleri ve kimya anabilim dallarının sentezlenmesi ile ortaya çıkan bir alandır. Kimyasal İsimlendirilmiş Nesne (KİN) tanımı kimya alanında yapılan yayınlardan ve patentlerden bilgi çıkarmanın ilk adımını oluşturur. Bu tezde KİN için Vektör Destek Makineleri (VDM) tabanlı ve sarıcı yöntemlerine dayalı öznitelik alt kümesi seçme algoritmaları kullanılan bir sınıflandırıcı sistemi önerilmiştir. Kimyasal isimlendirilmş nesneleri tanımlamak için kullanılacak VDM sınıflandırıcısını eğitmek ve sistemin başarımını ölçmek için derlemlere ihtiyaç vardır. Bu çalışmada iki-sınıf ve çok-sınıf sınıflandırıcı problemlerini incelemek adına farklı sayıda sınıflar içeren üç farklı kimyasal isimler içeren derlem kullanılmıştır. Eniyi öznitelik alt kümesini elde edbilmek için sargı yöntemine dayalı algoritmalar olarak İleri Seçim, Geri Seçim ve Basitleştirilmiş İleri Seçim algoritmaları kullanılmıştır. Kullanılan öznitelikler çeşitli yapılarda morfolojik, sözlüksel, ortografik ve boşluklardan oluşmaktadır. Bu çalışmada yapılan deneylerin amacı farklı öznitelik alt kümeleri kullanılarak elde edilecek sınıflandırıcı başarılarını incelemenin yanısıra KİN için varolan en uygun derlemi ortaya çıkarmaktır. Sonuçlar İleri Seçim algoritmasının sınıflandırma başarımını en etkin şekilde artıran öznitelik kümesini göstermiştir. Anahtar Kelimeler: Kimyasal İsimlendirilmiş Nesne Tanımı, Öznitelik Çıkarma, Sarıcı Yöntemlerine Dayalı Öznitelik Alt Kümesi Seçme, Vektör Destek Makineleri, Metin Madenciliği. Master of Science in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering, 2013. Supervisor: Assoc. Prof. Dr. Ekrem Varoğlu.

Published
2013

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