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Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracted is naturally imbalanced since chemical entities are fewer compared to other segments in text. In this paper, the class imbalance problem in the context of chemical named entity recognition has been studied and adopted version of random undersampling for NER data, has been leveraged to generate a pool of classifiers. In order to keep the classes’ distribution balanced within each sentence, the well-known random undersampling method is modified to a sentence based version where the random removal of samples takes place within each sentence instead of considering the dataset as a whole. Furthermore, to take the advantages of combination of a set of diverse predictors, an ensemble of classifiers trained with the set of different training data resulted by sentence-based undersampling, is created. The proposed approach is developed and tested using the ChemDNER corpus released by BioCreative IV. Results show that the proposed method improves the classification performance of the baseline classifiers mainly as a result of an increase in recall. Furthermore, the combination of high performing classifiers trained using undersampled train data surpasses the performance of all single best classifiers and the combination of classifiers using full data.

Chemical Named Entity Recognition (ChemNER) is the first step for a large number of consequent Information Extraction (IE) tasks in the chemistry related sciences and drug development domains. Extraction of drug-drug interactions, chemical compounds‘ resolution, and creation of question answering systems are examples of such applications. Any improvement in the quality of NER process in this context may affect the performance of subsequent tasks which shows the importance of this preliminary step in IE applications. In this thesis we studied this problem by proposing a modular architecture to improve the performance of ChemNER systems. This thesis has three main contributions to the overall task. The first contribution is the design of a new rule based tokenizer which improves the quality of data preprocessing phase. Due to the highly imbalanced nature of the data used in the NER task, overall performance of the classifiers used is usually not as good as those used in some other common classification tasks. Hence, a new sentence based undersampling approach specifically to be used for the NER problems is proposed as the second contribution for the given problem. The proposed undersampling approach tries to remove the insignificant samples from the training data aiming at preserving the structure of the given sentences as much as possible. We name it as Balance Undersampling (BUS) approach since it tries to keep almost an equal number of negative samples surrounding the positives. The third contribution of this thesis is to use the Particle Swarm Optimization algorithm as a heuristic classifier selection method together with the Naïve Bayesian combination approach to form a classifier ensemble from a large pool of classifiers created using undersampled data with different sampling ratios and various feature sets. All experiments during this study are conducted using the BioCreative IV ChemDNER corpus which is the most comprehensive data set in the domain. ÖZ: Kimsayal Adlandırılmış Varlık Tanıma (KAVT) kimya ve eczacılık ile ilgili alanlarda bilgi çıkarımı öncesi yapılması gereken ilk işlemlerden biridir. İlaçlar arası etkileşimlerin çıkarılması, kimyasal bileşenlerin çözünürlüğünün ortaya çıkarılması ve otomatik soru-cevap sistemlerinin yapımı bu işlemlerden bazılarıdır. Bu sebepten dolatyı KAVT basamağında yapılacak tüm iyileştirmeler, takip eden sistemlerinin başarısını büyük ölçüde etkilemektedir. Bu tezde KAVT problemi ele alınmış ve KAVT sistemlerinin başarımını artırmak için birimsel bir mimari önerilmiştir. Bu anlamda tezin literatüre üç temel katkısı vardır. Birinci katkı olarak metin önişleme işlemleri sırasında performamsı artırmak için yeni bir kural-tabanlı alıntı ayırıcı önerilmiştir. KAVT işleminde kullanılan verinin doğal nedenlerle sınıflar arası dengesiz olmasından dolayı, sınıflandırıcıların başarımı genellikle yüksek olmamaktadır. Bu nedenle, ikinci katkı olarak cümle-tabanlı yeni bir alt-örnekleme yöntemi önerilmiştir. Önerilen yöntem, eğitme veri kümesinde bulunan önemsiz örnekleri cümlenin yapısını en az bozacak şekilde çalışmaktadır. Tüm olumlu örneklerin sağ ve sol taraflarından eşit miktarda olumsuz örneği eğitme veri kümesinden çıkardığı için önerilen yönteme Dengeli Alt-örnekleme (DAÖ) ismi verlimiştir. Üçüncü katkı ise, çoklu sınıflandırcı yöntemi kullanılmasıdır. Bu yöntemin kullanılmasında Parçacık Apaçık Eniyileme yöntemi algoritması sınıflandırıcı seçimi için kullanılmış, seçilen sınıflandırıcılar ise Bayesçi Birleştirme yöntemi ile birleştirilerek alt-örneklenmiş örnekler kullanılarak eğitilmiş büyük bir sınıflandırıcı topluluğu elde edilmiştir. Bu çalışmada, ilgili alanda en büyük bütünce olarak bilinen BioCreative IV ChemDNER bütüncesi kullanılmıştır. Doctor of Philosophy in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering, 2016. Co-Supervisor: Assist. Prof. Dr. Nazife Dimililer and Supervisor: Assoc. Prof. Dr. Ekrem Varoğlu.

With the constantly growing amount of biomedical literature, methods for automatically distilling information from unstructured data, collectively known as information extraction, have become indispensable. Whilst most biomedical information extraction efforts in the last decade have focussed on the identification of gene products and interactions between them, the biomedical text mining community has recently extended their scope to capture associations between biomedical and chemical entities with the aim of supporting applications in drug discovery. This thesis is the first comprehensive study focussing on information extraction from pharmaceutical chemistry literature. In this research, we describe our work on (1) recognising names of chemical compounds and drugs, facilitated by the incorporation of domain knowledge; (2) exploring different coreference resolution paradigms in order to recognise co-referring expressions given a full-text article; and (3) defining drug-target interactions as events and distilling them from pharmaceutical chemistry literature using event extraction methods. With the constantly growing amount of biomedical literature, methods for automatically distilling information from unstructured data, collectively known as information extraction, have become indispensable. Whilst most biomedical information extraction efforts in the last decade have focussed on the identification of gene products and interactions between them, the biomedical text mining community has recently extended their scope to capture associations between biomedical and chemical entities with the aim of supporting applications in drug discovery. This thesis is the first comprehensive study focussing on information extraction from pharmaceutical chemistry literature. In this research, we describe our work on (1) recognising names of chemical compounds and drugs, facilitated by the incorporation of domain knowledge; (2) exploring different coreference resolution paradigms in order to recognise co-referring expressions given a full-text article; and (3) defining drug-target interactions as events and distilling them from pharmaceutical chemistry literature using event extraction methods.

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.

Background The development of robust methods for chemical named entity recognition, a challenging natural language processing task, was previously hindered by the lack of publicly available, large-scale, gold standard corpora. The recent public release of a large chemical entity-annotated corpus as a resource for the CHEMDNER track of the Fourth BioCreative Challenge Evaluation (BioCreative IV) workshop greatly alleviated this problem and allowed us to develop a conditional random fields-based chemical entity recogniser. In order to optimise its performance, we introduced customisations in various aspects of our solution. These include the selection of specialised pre-processing analytics, the incorporation of chemistry knowledge-rich features in the training and application of the statistical model, and the addition of post-processing rules. Results Our evaluation shows that optimal performance is obtained when our customisations are integrated into the chemical entity recogniser. When its performance is compared with that of state-of-the-art methods, under comparable experimental settings, our solution achieves competitive advantage. We also show that our recogniser that uses a model trained on the CHEMDNER corpus is suitable for recognising names in a wide range of corpora, consistently outperforming two popular chemical NER tools. Conclusion The contributions resulting from this work are two-fold. Firstly, we present the details of a chemical entity recognition methodology that has demonstrated performance at a competitive, if not superior, level as that of state-of-the-art methods. Secondly, the developed suite of solutions has been made publicly available as a configurable workflow in the interoperable text mining workbench Argo. This allows interested users to conveniently apply and evaluate our solutions in the context of other chemical text mining tasks.