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American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
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In: Sensors ; Volume 21 ; Issue 17 (2021)
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Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
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In: Multimodal Technologies and Interaction ; Volume 4 ; Issue 1 (2020)
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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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Interactional Metadiscourse In Doctoral Thesis Writing: A Study in Kenya
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In: Applied Linguistics Research Journal, Vol 4, Iss 4, Pp 100-113 (2020) (2020)
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Computing Happiness from Textual Data
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In: Stats ; Volume 2 ; Issue 3 ; Pages 25-370 (2019)
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Arabic-SOS: Segmentation, stemming, and orthography standardization for classical and pre-modern standard Arabic
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Computing Happiness from Textual Data
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In: 2 ; 3 ; 347 ; 370 (2019)
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IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
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In: DeFT 2017 - Défi Fouille de texte ; https://hal.archives-ouvertes.fr/hal-01643993 ; DeFT 2017 - Défi Fouille de texte, Jun 2017, Orléans, France. pp.1-10 (2017)
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The Functions of Narrative Passages in Three Written Online Health Contexts
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In: Open Linguistics, Vol 2, Iss 1 (2016) (2016)
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ОБЗОР МЕТОДОВ И АЛГОРИТМОВ РАЗРЕШЕНИЯ ЛЕКСИЧЕСКОЙ МНОГОЗНАЧНОСТИ: ВВЕДЕНИЕ
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IRISA at DeFT 2015: Supervised and Unsupervised Methods in Sentiment Analysis
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In: DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015 ; https://hal.archives-ouvertes.fr/hal-01226528 ; DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015, Jun 2015, Caen, France (2015)
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A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
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A nonparametric Bayesian perspective for machine learning in partially-observed settings
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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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Sign Language Recognition using Sub-Units
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In: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/2012/Cooper_JMLR_2012.pdf (2012)
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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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The ICSI+ Multilingual Sentence Segmentation System
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In: DTIC (2006)
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