Data Mining Practical Machine Learning Tools and Techniques

Principles of Language Learning and Teaching

https://waseda.repo.nii.ac.jp/?action=repository_action_common_download&item_id=12133&item_no=1&attribute_id=162&file_no=1

言語運用能力(Communicative Competence)

Acknowledgements Introduction PART ONE - BACKGROUND Introduction 1. Second language acquisition research: an overview PART TWO - THE DESCRIPTION OF LEARNER LANGUAGE Introduction 2. Learner errors and error analysis 3. Developmental patterns: order and sequence in second language acquisition 4. Variability in learner language 5. Pragmatic aspects of learner language PART THREE - EXPLAINING SECOND LANGUAGE ACQUISITION: EXTERNAL FACTORS Introduction 6. Social factors and second language acquisition 7. Input and interaction and second language acquisition PART FOUR - EXPLAINING SECOND LANGUAGE ACQUISITION: INTERNAL FACTORS Introduction 8. Language transfer 9. Cognitive accounts of second language acquisition 10. Linguistic universals and second language acquisition PART FIVE - EXPLAINING INDIVIDUAL DIFFERENCES IN SECOND LANGUAGE ACQUISITION Introduction 11. Individual learner differences 12. Learning strategies PART SIX - CLASSROOM SECOND LANGUAGE ACQUISITION Introduction 13. Classroom interaction and second language acquisition 14. Formal instruction and second language acquisition PART SEVEN - CONCLUSION Introduction 15. Data, theory, and applications in second language acquisition research Glossary Bibliography Author index Subject index

第二语言习得研究 = The study of second language acquisition

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

Inductive learning algorithms and representations for text categorization

Data pre-processing presents the most time consuming phase in the whole process of knowledge discovery. The complexity of data pre-processing depends on the data sources used. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in e-documents. We used the transaction/sequence model for text representation and sequence rule analysis as a method of modelling. We compare four datasets of different quality obtained from texts and pre-processed in different ways: data with identified the paragraph sequences, data with identified the sentence sequences, data with identified the paragraph sequences without stop words and data with identified the sentence sequences without stop words. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules. The results confirm some initial assumptions, but they also show that the stop words removal has a substantial impact on the quantity and quality of extracted rules in case of paragraph sequence identification. Contrary, in case of sentence sequence identification, removing the stop words has not any significant impact on the quantity and quality of extracted rules.

Data Pre-processing Evaluation for Text Mining: Transaction/Sequence Model

Short texts like advertisements are characterised by a number of slogans, phrases, words, symbols etc. To improve the quality of textual data, it is necessary to filter out noise textual data from important data. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in English and Slovak advertisement corpora. For this purpose, an experiment was conducted focusing on data pre-processing in these two comparable corpora. We try to find out to what extent removing the stop words has an influence on a quantity and quality of extracted rules. Stop words removal has no impact on the quantity and quality of extracted rules in English as well as in Slovak advertisement corpora. Only language has a significant impact on the quantity and quality of extracted rules.

Influence of Stop-Words Removal on Sequence Patterns Identification within Comparable Corpora

Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy

Evaluation plays a crucial role in the field of machine translation The aim of our research was to assess the quality — reliability and validity — of automatic evaluation of machine translation, and in our case an on-line statistical machine translation system was used The results of the reliability analysis showed that the chosen metrics of automatic evaluation are reliable and valid, whereby the validity and reliability were verified for the following language pairs: Slovak-English and Slovak-German

Daša Munková

Papers

Data Pre-processing Evaluation for Text Mining: Transaction/Sequence Model

Influence of Stop-Words Removal on Sequence Patterns Identification within Comparable Corpora

Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy

An automatic evaluation of machine translation and Slavic languages

Detecting errors in machine translation using residuals and metrics of automatic evaluation