An Active Co-Training Algorithm for Biomedical Named-Entity Recognition

Tsendsuren Munkhdalai,Mei Jing Li,Un Il Yun,Oyun Erdene Namsrai,Keun Ho Ryu 한국정보처리학회 2012 Journal of information processing systems Vol.8 No.4

Self-training in significance space of support vectors for imbalanced biomedical event data

Munkhdalai, Tsendsuren,Namsrai, Oyun-Erdene,Ryu, Keun Ho BioMed Central 2015 BMC bioinformatics Vol.16 No.suppl7

<P><B>Background</B></P><P>Pairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. However, there are two critical issues that are seldom dealt with by existing systems. First, an annotated corpus for training a prediction model is highly imbalanced. Second, supervised models trained on only a single annotated corpus can limit system performance. Fortunately, there is a large pool of unlabeled data containing much of the domain background that one can exploit.</P><P><B>Results</B></P><P>In this study, we develop a new semi-supervised learning method to address the issues outlined above. The proposed algorithm efficiently exploits the unlabeled data to leverage system performance. We furthermore extend our algorithm to a two-phase learning framework. The first phase balances the training data for initial model induction. The second phase incorporates domain knowledge into the event extraction model. The effectiveness of our method is evaluated on the Genia event extraction corpus and a PubMed document pool. Our method can identify a small subset of the majority class, which is sufficient for building a well-generalized prediction model. It outperforms the traditional self-training algorithm in terms of f-measure. Our model, based on the training data and the unlabeled data pool, achieves comparable performance to the state-of-the-art systems that are trained on a larger annotated set consisting of training and evaluation data.</P>

An Active Co-Training Algorithm for Biomedical Named-Entity Recognition

Munkhdalai, Tsendsuren,Li, Meijing,Yun, Unil,Namsrai, Oyun-Erdene,Ryu, Keun Ho Korea Information Processing Society 2012 Journal of information processing systems Vol.8 No.4

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

바이오 문헌에서의 단백질, 유전자 객체 인식을 위한 특징 추출

김태욱,이미정,Munkhdalai Tsendsuren,류근호 한국정보과학회 2012 한국정보과학회 학술발표논문집 Vol.39 No.1C

A Feature Selection-based Ensemble Method for Arrhythmia Classification

Erdenetuya Namsrai,Tsendsuren Munkhdalai,Meijing Li,Jung Hoon Shin,Oyun Erdene Namsrai,Keun Ho Ryu 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.1

In this paper a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method the feature selection rate depends on the extracting order of the feature subsets. In the experiment we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

A Feature Selection-based Ensemble Method for Arrhythmia Classification

Namsrai, Erdenetuya,Munkhdalai, Tsendsuren,Li, Meijing,Shin, Jung-Hoon,Namsrai, Oyun-Erdene,Ryu, Keun Ho Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.1

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Li, Meijing,Munkhdalai, Tsendsuren,Yu, Xiuming,Ryu, Keun Ho Hindawi Limited 2015 Mathematical problems in engineering Vol.2015 No.-

<P>Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.</P>

A Dependency Graph-Based Keyphrase Extraction Method Using Anti-patterns

Batsuren, Khuyagbaatar,Batbaatar, Erdenebileg,Munkhdalai, Tsendsuren,Li, Meijing,Namsrai, Oyun-Erdene,Ryu, Keun Ho Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.5

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

A Dependency Graph-Based Keyphrase Extraction Method Using Anti-patterns

( Khuyagbaatar Batsuren ),( Erdenebileg Batbaatar ),( Tsendsuren Munkhdalai ),( Meijing Li ),( Oyun-erdene Namsrai ),( Keun Ho Ryu ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.5

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many textmining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.