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Extracting Chinese Multi-word Terms from Small Corpus

Extracting Chinese Multi-word Terms from Small Corpus

引用

3rd international conference on Intelligent System and Knowledge Engineering

作者： Lang, Zhou Liang, Zhang Chong, Feng Heyan, Huang Nanjing Univ Sci Technol Coll Comp Sci & Technol Nanjing 210094 Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing 210093 Peoples R China Res Ctr Comp & Language Informat Engn CAS Beijing 100089 Peoples R China

ISBN: (纸本)9781424421961

In this paper, we present an automatic terminology extraction approach for Chinese multi-word terms. In this term extraction system, besides five linguistic rules acquired from an available term list by some machine learning methods, two statistical strategies are involved: a termhood measure based on the term distribution variation, and a unithood measure adopting the left and right entropy method to estimate the collocation variation degree. The candidates are ranked according to the values of the former. The latter is used to filter the preposition phrases and some verb-object phrases that rarely appear as terms. By validating on a small scale corpus in the computer domain, the precision reaches 91.5% of the top 2000 outputs.

关键词： data mining Terminology Knowledge engineering Filters Intelligent systems Computer science Measurement units Statistical analysis Testing Natural languages

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Protein Expression Molecular pattern Discovery by Nonnegative Principal Component Analysis

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Han, Xiaoxu Scazzero, Joseph Eastern Michigan Univ Dept Math & Bioinformat Program Ypsilanti MI 48197 USA Eastern Michigan Univ Dept Accout & Finance Ypsilanti MI USA

ISBN: (纸本)9783540884347

Identifying cancer molecular patterns robustly from large dimensional protein expression data not only has significant impacts on clinical ontology, but also presents a challenge for statistical learning. Principal component analysis (PCA) is a widely used feature selection algorithm and generally integrated with classic classification algorithms to conduct cancer molecular pattern discovery. However, its holistic mechanism prevents local data characteristics capture in feature selection. This may lead to the increase of misclassification rates and affect robustness of cancer molecular diagnostics. In this study, we develop a nonnegative principal component analysis (NPCA) algorithm and propose a NPCA-based SVM algorithm with sparse coding in the cancer molecular pattern analysis of proteomics data. We report leading classification results from this novel algorithm in predicting cancer molecular patterns of three benchmark proteomics datasets, under 100 trials of 50% hold-out and leave one out cross validations, by directly comparing its performances with those of the PCA-SVM, NMF-SVM, SVM, k-NN and PCA-LDA classification algorithms with respect to classification rates, sensitivities and specificities. Our algorithm also overcomes the overfitting problem in the SVM and PCA-SVM classifications and provides exceptional sensitivities and specificities.

关键词： Nonnegative principle component analysis sparse coding support vector machine (SVM)

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Feature Selection and Classification for Small Gene Sets

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3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Stiglic, Gregor Rodriguez, Juan J. Kokol, Peter Univ Maribor Fac Hlth Sci Zitna Ulica 15 SLO-2000 Maribor Slovenia Univ Maribor Fac Elect Engn & Comp Sci Zitna Ulica 15 SLO-2000 Maribor Slovenia Univ Burgos Burgos 09006 Spain

ISBN: (纸本)9783540884347

Random Forests, Support Vector machines and k-Nearest Neighbors are successful and proven classification techniques that are widely used for different kinds of classification problems. One of them is classification of genomic and proteomic data that is known as a problem with extremely high dimensionality and therefore demands suited classification techniques. In this domain they are usually combined with gene selection techniques to provide optimal classification accuracy rates. Another reason for reducing the dimensionality of such datasets is their interpretability. It is much easier to interpret a small set of ranked genes than 20 or 30 thousands of unordered genes. In this paper we present a classification ensemble of decision trees called Rotation Forest and evaluate its classification performance on small subsets of ranked genes for 14 genomic and proteomic classification problems. An important feature of Rotation Forest is demonstrated - i.e. robustness and high classification accuracy using small sets of genes.

关键词： Gene expression analysis machine learning feature selection ensemble of classifiers

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Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Gromiha, M. Michael Huang, Liang-Tsung Lai, Lien-Fu Natl Inst Adv Ind Sci & Technol Computat Biol Res Ctr Koto Ku AIST Tokyo Waterfront Bio IT Res Bldg2-42 Aomi Tokyo 1350064 Japan MingDao Univ Dept Comp Sci & Informat Engn Changhua 523 Taiwan Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua 523 Taiwan

ISBN: (纸本)9783540884347

Prediction of protein stability upon amino acid substitution and discrimination of thermophilic proteins from mesophilic ones are important problems in designing stable proteins. We have developed a classification rule generator using the information about wild-type, mutant, three neighboring residues and experimentally observed stability data. Utilizing the rules, we have developed a method based on decision tree for discriminating the stabilizing and destabilizing mutants and predicting protein stability changes upon single point mutations, which showed an accuracy of 82% and a correlation of 0.70, respectively. In addition, we have systematically analyzed the characteristic features of amino acid residues in 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively, and developed methods for discriminating them. The method based on neural network could discriminate them at the 5-fold cross-validation accuracy of 89% in a dataset of 4684 proteins and 91% in a test set of 707 proteins.

关键词： Protein stability rule generator discrimination prediction thermophilic proteins neural network machine learning techniques

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Applications of average geodesic distance in manifold learning

引用

3rd international conference on Rough Sets and Knowledge Technology, RSKT 2008

作者： Zeng, Xianhua School of Computer Science China West Normal University Nanchong 637002 China

ISBN: (纸本)3540797203

Manifold learning has become a hot issue in the research fields of machine learning and data mining. Current manifold learning algorithms assume that the observed data set has the high density. But, how to evaluate the denseness of the high dimensional observed data set? This paper proposes an algorithm based on the average geodesic distance as the preprocessing step of manifold learning. Moreover, for a high dense data set evaluated, we further utilize the average geodesic distance to quantitatively analyze the mapping relationship between the high-dimensional manifold and the corresponding intrinsic low-dimensional manifold in the known ISOMAP algorithm. Finally, experimental results on two synthetic Swiss-roll data sets show that our method is feasible. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： learning systems

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Named entity recognition for South Asian languages 3

Named entity recognition for South Asian languages

引用

IJCNLP Workshop on Named Entity recognition for South and South East Asian Languages, held in conjunction with the 3rd international Joint conference on Natural Language Processing, IJCNLP 2008

作者： Goyal, Amit University of Utah School of Computing Salt Lake CityUT United States

Much work has already been done on building named entity recognition systems. However most of this work has been concentrated on English and other European languages. Hence, building a named entity recognition (NER) system for South Asian Languages (SAL) is still an open problem because they exhibit characteristics different from English. This paper builds a named entity recognizer which also identifies nested name entities for the Hindi language using machine learning algorithm, trained on an annotated corpus. However, the algorithm is designed in such a manner that it can easily be ported to other South Asian Languages provided the necessary NLP tools like POS tagger and chunker are available for that language. I compare results of Hindi data with English data of CONLL shared task of 2003. © 2008 Asian Federation of Natural Language Processing.

关键词： learning algorithms

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3rd international conference on machine learning and data mining in pattern recognition, MLDM 2003

引用

3rd international conference on machine learning and data mining, MLDM 2003

ISBN: (纸本)3540405046

The proceedings contain 37 papers. The special focus in this conference is on Decision Trees, Clustering and Its Application. The topics include: Introspective learning to build case-based reasoning CBR knowledge containers;graph-based tools for data mining and machine learning;simplification methods for model trees with regression and splitting nodes;learning multi-label alternating decision trees from texts and data;a discretization method of continuous attributes with guaranteed resistance to noise;on the size of a classification tree;a comparative analysis of clustering algorithms applied to load profiling;similarity-based clustering of sequences using hidden markov models;a fast parallel optimization for training support vector machine;a ROC-based reject rule for support vector machines;remembering similitude terms in CBR;authoring cases from free-text maintenance data;classification boundary approximation by using combination of training steps for real-time image segmentation;simple mimetic classifiers;novel mixtures based on the dirichlet distribution;estimating a quality of decision function by empirical risk;efficient locally linear embeddings of imperfect manifolds;dissimilarity representation of images for relevance feedback in content-based image retrieval;a rule-based scheme for filtering examples from majority class in an imbalanced training set;coevolutionary feature learning for object recognition;generalization of pattern-growth methods for sequential pattern mining with gap constraints;discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle and optimizing financial portfolios from the perspective of mining temporal structures of stock returns.

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Multi-relational data mining for Tetratricopeptide Repeats (TPR)-Like Superfamily Members in Leishmania spp.: Acting-by-Connecting Proteins

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Girao, Karen T. Oliveira, Fatima C. E. Farias, Kaio M. Maia, Italo M. C. Silva, Samara C. Gadelha, Carla R. F. Carneiro, Laura D. G. Pacheco, Ana C. L. Kamimura, Michel T. Diniz, Michely C. Silva, Maria C. Oliveira, Diana M. Univ Estadual Ceara UECE Nucleo Tarcisio Pimenta Pesquisa Genom & Bioinfor NUGEN Fac Vet BR-60740000 Fortaleza Ceara Brazil

ISBN: (纸本)9783540884347

The multi-relational data mining (MrdM) approach looks for patterns that involve multiple tables from a relational database made of complex/structured objects whose normalized representation does require multiple tables. We have applied MrdM methods (relational association rule discovery and probabilistic relational models) with hidden Markov models (HMMs) and Viterbi algorithm (VA) to mine tetratricopeptide repeat (TPR), pentatricopeptide (PPR) and half-a-TPR (HAT) in genomes of pathogenic protozoa Leishmania. TPR is a protein-protein interaction module and TPR-containing proteins (TPRPs) act as scaffolds for the assembly of different multiprotein complexes. Our aim is to build a great panel of the TPR-like superfamily of Leishmania. Distributed relational state representations for complex stochastic processes were applied to identification, clustering and classification of Leishmania genes and we were able to detect putative 104 TPRPs, 36 PPRPs and 08 HATPs, comprising the TPR-like superfamily. We have also compared currently available resources (Pfam, SMART, SUPERFAMILY and TPRpred) with our approach (MrdM/HMM/VA).

关键词： Multi-relational data raining hidden Markov models Viterbi algorithm tetratricopeptide repeat motif Leishmania proteins

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Contents

Contents

引用

international conference on Intelligent System and Knowledge Engineering, ISKE

The following topics are dealt with: AI and expert systems; artificial immune systems and bio-informatics; chaos theory; data mining; fuzzy set theory; genetic algorithm; information retrieval; intelligent control; intelligent decision making; intelligent information processing; intelligent recognition; intelligent robotics; machine learning; natural language & machine translation; neural networks; rought set theory; support vector machine; swarm intelligence; video and image processing.

关键词： Artificial intelligence Intelligent robots Intelligent networks learning systems Intelligent control Expert systems Artificial immune systems Chaos data mining Fuzzy set theory

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A Survey on Statistical pattern Feature Extraction

引用

4th international conference on Intelligent Computing

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Jin, Fengxiang Shi, Zhongzhi China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221008 Peoples R China Acad Sinica Key Lab Intelligent Informat Proc Inst Comop Technol Beijing 100080 Peoples R China Agr Univ Coll Plant Protection Tai An Shandong 271018 Peoples R China Shandong Univ Sci Technol Coll Geoinformation Sci Engn Qingdao 266510 Peoples R China

ISBN: (纸本)9783540859833

The goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. This paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.

关键词： Extraction

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