scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

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.

scholarly journals Identifying interactions between chemical entities in biomedical text

2014 ◽  
Vol 11 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Andre Lamurias ◽  
João D. Ferreira ◽  
Francisco M. Couto
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Ensemble Classifier ◽  
Entity Recognition ◽  
Support Vector ◽  
Biomedical Text ◽  
Web Tool ◽  
Named Entity ◽  
Vector Machines ◽  
Chemical Named Entity Recognition

Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

scholarly journals Named Entity Recognition for Sensitive Data Discovery in Portuguese

2020 ◽  
Vol 10 (7) ◽  
pp. 2303 ◽  
Author(s):  
Mariana Dias ◽  
João Boné ◽  
João C. Ferreira ◽  
Ricardo Ribeiro ◽  
Rui Maia
Keyword(s):  
Random Fields ◽  
Statistical Models ◽  
Conditional Random Fields ◽  
Hybrid Approach ◽  
Named Entity Recognition ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Sensitive Data ◽  
Rule Based ◽  
Named Entity

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested—Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus.

scholarly journals COST-SENSITIVE STRUCTURED PERCEPTRON INCORPORATING CATEGORY HIERARCHY FOR NAMED ENTITY RECOGNITION

2015 ◽  
Author(s):  
Shohei Higashiyama ◽  
Blondel Mathieu ◽  
Kazuhiro Seki ◽  
Kuniaki Uehara
Keyword(s):  
Cost Function ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Named Entity ◽  
Vector Machines ◽  
Representative Work ◽  
Correct Category

Named Entity Recognition (NER) is a fundamental natural language processing task for the identifi cation and classifi cation of expressions into predefi ned categories, such as person and organization. Existing NER systems usually target about 10 categories and do not incorporate analysis of category relations. However, categories often belong naturally to some predefi ned hierarchy. In such cases, the distance between categories in the hierarchy becomes a rich source of information that can be exploited. This is intuitively useful particularly when the categories are numerous. On that account, this paper proposes an NER approach that can leverage category hierarchy information by introducing, in the structured perceptron framework, a cost function more strongly penalizing category predictions that are more distant from the correct category in the hierarchy. Experimental results on the GENIA biomedical text corpus indicate the effectiveness of the proposed approach as compared with the case where no cost function is utilized. In addition, the proposed approach demonstrates the superior performance over a representative work using multi-class support vector machines on the same corpus. A possible direction to further improve the proposed approach is to investigate more elaborate cost functions than a simple additive cost adopted in this work.  

scholarly journals NAMED ENTITY RECOGNITION IN GREEK TEXTS WITH AN ENSEMBLE OF SVMS AND ACTIVE LEARNING

2007 ◽  
Vol 16 (06) ◽  
pp. 1015-1045 ◽  
Author(s):  
GIORGIO LUCARELLI ◽  
XENOFON VASILAKOS ◽  
ION ANDROUTSOPOULOS
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Support Vector ◽  
Named Entity ◽  
Input Text ◽  
Vector Machines ◽  
Temporal Expressions ◽  
General Collection

We present a freely available named-entity recognizer for Greek texts that identifies temporal expressions, person, and organization names. For temporal expressions, it relies on semi-automatically produced patterns. For person and organization names, it employs an ensemble of Support Vector Machines that scan the input text in two passes. The ensemble is trained using active learning, whereby the system itself proposes candidate training instances to be annotated by a human during training. The recognizer was evaluated on both a general collection of newspaper articles and a more focussed, in terms of topics, collection of financial articles.

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