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 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.

SCIENTIFIC NAMED ENTITY RECOGNITION WITH THE HELP OF MODERN METHODS

2021 ◽  
Vol 75 (3) ◽  
pp. 94-99
Author(s):  
A.M. Yelenov ◽  
◽  
A.B. Jaxylykova ◽  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Support Vector ◽  
Scientific Article ◽  
Natural Languages ◽  
Named Entity ◽  
Learning Area

This research focuses on a comparative study of the Named Entity Recognition task for scientific article texts. Natural language processing could be considered as one of the cornerstones in the machine learning area which devotes its attention to the problems connected with the understanding of different natural languages and linguistic analysis. It was already shown that current deep learning techniques have a good performance and accuracy in such areas as image recognition, pattern recognition, computer vision, that could mean that such technology probably would be successful in the neuro-linguistic programming area too and lead to a dramatic increase on the research interest on this topic. For a very long time, quite trivial algorithms have been used in this area, such as support vector machines or various types of regression, basic encoding on text data was also used, which did not provide high results. The following dataset was used to process the experiment models: Dataset Scientific Entity Relation Core. The algorithms used were Long short-term memory, Random Forest Classifier with Conditional Random Fields, and Named-entity recognition with Bidirectional Encoder Representations from Transformers. In the findings, the metrics scores of all models were compared to each other to make a comparison. This research is devoted to the processing of scientific articles, concerning the machine learning area, because the subject is not investigated on enough properly level.The consideration of this task can help machines to understand natural languages better, so that they can solve other neuro-linguistic programming tasks better, enhancing scores in common sense.

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 Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 82
Author(s):  
SaiKiranmai Gorla ◽  
Lalita Bhanu Murthy Neti ◽  
Aruna Malapati
Keyword(s):  
Language Processing ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Limited Resources ◽  
Support Vector ◽  
Named Entity ◽  
Word Level ◽  
Asian Languages ◽  
Better Than

Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for South-East Asian languages like Telugu is quite a challenging problem. This paper attempts to improve the NER performance for Telugu using gazetteer-related features, which are automatically generated using Wikipedia pages. We make use of these gazetteer features along with other well-known features like contextual, word-level, and corpus features to build NER models. NER models are developed using three well-known classifiers—conditional random field (CRF), support vector machine (SVM), and margin infused relaxed algorithms (MIRA). The gazetteer features are shown to improve the performance, and theMIRA-based NER model fared better than its counterparts SVM and CRF.

scholarly journals Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Na Deng ◽  
Hao Fu ◽  
Xu Chen
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Language Processing ◽  
Patent Application ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Superior Performance ◽  
Practical Significance ◽  
Therapeutic Effects ◽  
Named Entity

With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.

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