| 1 |
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
|
|
|
|
In: Sensors ; Volume 21 ; Issue 17 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
|
|
|
|
In: Multimodal Technologies and Interaction ; Volume 4 ; Issue 1 (2020)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
La potenciación descortés del desacuerdo en hablantes españoles e ingleses ; Impolite boosting of disagreement in Spanish and English speakers
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Interactional Metadiscourse In Doctoral Thesis Writing: A Study in Kenya
|
|
|
|
In: Applied Linguistics Research Journal, Vol 4, Iss 4, Pp 100-113 (2020) (2020)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Computing Happiness from Textual Data
|
|
|
|
In: Stats ; Volume 2 ; Issue 3 ; Pages 25-370 (2019)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Arabic-SOS: Segmentation, stemming, and orthography standardization for classical and pre-modern standard Arabic
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Computing Happiness from Textual Data
|
|
|
|
In: 2 ; 3 ; 347 ; 370 (2019)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
The Functions of Narrative Passages in Three Written Online Health Contexts
|
|
|
|
In: Open Linguistics, Vol 2, Iss 1 (2016) (2016)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
ОБЗОР МЕТОДОВ И АЛГОРИТМОВ РАЗРЕШЕНИЯ ЛЕКСИЧЕСКОЙ МНОГОЗНАЧНОСТИ: ВВЕДЕНИЕ
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
IRISA at DeFT 2015: Supervised and Unsupervised Methods in Sentiment Analysis
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
All cumulative semantic interference is not equal: A test of the Dark Side Model of lexical access
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Sign Language Recognition using Sub-Units
|
|
|
|
In: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/2012/Cooper_JMLR_2012.pdf (2012)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Boosting of fuzzy rules with low quality data
|
|
|
|
In: http://sci2s.ugr.es/publications/ficheros/JMVLSC2011.pdf (2011)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Adasum: an adaptive model for summarization
|
|
|
|
In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2008/p901-zhang.pdf (2008)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006
|
|
|
|
In: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf (2006)
|
|
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.
|
|
Keyword:
Boosting; Named Entity Recognition; NER
|
|
URL: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.8450
|
|
BASE
|
|
Hide details
|
|
|
| 20 |
The ICSI+ Multilingual Sentence Segmentation System
|
|
|
|
In: DTIC (2006)
|
|
BASE
|
|
Show details
|
|
|
|
|
