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La potenciación descortés del desacuerdo en hablantes españoles e ingleses ; Impolite boosting of disagreement in Spanish and English speakers
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Arabic-SOS: Segmentation, stemming, and orthography standardization for classical and pre-modern standard Arabic
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All cumulative semantic interference is not equal: A test of the Dark Side Model of lexical access
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| 4 |
Boosting of fuzzy rules with low quality data
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In: http://sci2s.ugr.es/publications/ficheros/JMVLSC2011.pdf (2011)
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Adasum: an adaptive model for summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2008/p901-zhang.pdf (2008)
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A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006
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In: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf (2006)
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Abstract:
Abstract. In this paper we introduce a multilingual Named Entity Recognition (NER) system that uses statistical modeling techniques. The system identifies and classifies NEs in the Hungarian and English languages by applying AdaBoostM1 and the C4.5 decision tree learning algorithm. We focused on building as large a feature set as possible, and used a split and recombine technique to fully exploit its potentials. This methodology provided an opportunity to train several independent decision tree classifiers based on different subsets of features and combine their decisions in a majority voting scheme. The corpus made for the CoNLL 2003 conference and a segment of Szeged Corpus was used for training and validation purposes. Both of them consist entirely of newswire articles. Our system remains portable across languages without requiring any major modification and slightly outperforms the best system of CoNLL 2003, and achieved a 94.77 % F measure for Hungarian. The real value of our approach lies in its different basis compared to other top performing models for English, which makes our system extremely successful when used in combination with CoNLL modells.
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Keyword:
Boosting; Named Entity Recognition; NER
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URL: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.8450
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| 7 |
IEEE Instrumentation and Measurement
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In: http://www.research.ibm.com/voicemail/postscript/imtc2001.ps (2001)
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Evolution of the Performance of Automatic Speech Recognition Algorithms in Transcribing Conversational Telephone Speech
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In: http://www.research.ibm.com/people/m/mukund/postscript/imtc2001.ps (2001)
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| 9 |
Evolution of the performance of automatic speech recognition algorithms in transcribing conversational telephone speech
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In: http://research.microsoft.com/users/gzweig/pubs/imtc2001.pdf (2001)
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| 10 |
The explicit signalling of premise-conclusion sequences in research articles: a contrastive framework
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| 11 |
Automatic recognition of German news focusing on future-directed beliefs and intentions
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In: http://www.eckle-kohler.de/csl-textclassification.pdf
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| 12 |
A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms
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In: http://www.inf.u-szeged.hu/%7Eszarvas/homepage/pdf/multilingner.pdf
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Kocsor A: A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms
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In: http://www.inf.u-szeged.hu/~nistvan/bevinfo/hazi1/hungarianNER.pdf
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