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The Contemporary Wayang Archive (CWA) (http://cwa-web.org/en/) is a collection of re-elaborations of Java's oldest performance tradition. All of the performances were recorded in 21st century Java. This archive includes translations, notes and explanations of how the performances were received in their original context.The CWA is part of the Asian Intercultural Digital Archives (AIDA). It was developed as part of two projects funded by the Singapore Ministry of Education at the National University of Singapore: Relocating Intercultural Theatre (MOE2008-T2-1-110) and Digital Archiving and Intercultural Performance (MOE2013-t2-1-011). The CWA is a joint initiative with the Indonesian Visual Arts Archive (IVAA).For more details, please visit http://cwa-web.org/en/

We release the 4,811 Weibo image tweets with human annotated image-text relationship (i.e., visual/non-visual) used in the following paper. Please cite our MM'13 paper if you use this dataset. Tao Chen, Dongyuan Lu, Min-Yen Kan and Peng Cui (2013). Understanding and Classifying Image Tweets. In Proceedings of the 21st ACM International Conference on Multimedia (MM'13), Barcelona, Spain. For details, please visit https://github.com/WING-NUS/visual-image-tweets.

CoNLL-2013 will continue the CoNLL tradition of having a high profile shared task in natural language processing. This year's shared task will be grammatical error correction. A participating system in this shared task is given short English texts written by non-native speakers of English. The system detects the grammatical errors present in the input texts, and returns the corrected texts.This task has gained popularity recently with the organization of the HOO (Helping Our Own) shared tasks in 2011 and 2012. In the most recent HOO shared task in 2012, on error detection and correction of determiners and prepositions, 14 teams from around the world participated and 85 systems were submitted to the shared task.The grammatical error correction task is impactful since it is estimated that hundreds of millions of people in the world are learning English and they benefit directly from an automated grammar checker. However, for many error types, current grammatical error correction methods do not achieve a high performance and thus more research is needed.Instead of focusing on only determiner and preposition errors as in HOO 2012, the CoNLL-2013 shared task will include a more comprehensive list of error types, including determiner, preposition, noun number, verb form, and subject-verb agreement errors. Extending into more error types introduces the possibility of correcting multiple interacting errors. Examples of such interacting errors include determiner and noun number ('that cars' → 'that car' or 'those cars') and preposition and verb form ('an interest to study’ → ‘an interest in studying').Participating teams will be provided with common training data in which grammatical errors have been annotated. Blind test data will be used to evaluate the outputs of the participating teams using a common scoring software and evaluation metric.To download and reuse this dataset, please visit https://doi.org/10.25542/GVZ8-1MME.Related Publication: Ng, Hwee Tou, & Wu, Siew Mei, & Wu, Yuanbin, & Hadiwinoto, Christian, & Tetreault, Joel (2013). The CoNLL-2013 Shared Task on Grammatical Error Correction. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task (CoNLL-2013 Shared Task). Sofia, Bulgaria.

The NUS Corpus of Learner English (NUCLE) was collected in a collaboration project between the National University of Singapore (NUS) Natural Language Processing (NLP) Group led by Prof. Hwee Tou Ng and the NUS Centre for English Language Communication (CELC) led by Prof. Siew Mei Wu. The work was carried out as part of the PhD thesis research of Daniel Dahlmeier at the NUS NLP Group. The corpus consists of about 1,400 essays written by university students at the National University of Singapore on a wide range of topics, such as environmental pollution, healthcare, etc. It contains over one million words which are completely annotated with error tags and corrections. All annotations have been performed by professional English instructors at the NUS CELC. The corpus is distributed under the standard NUS licensing agreement the terms and conditions for which are provided below. Please read the terms and conditions carefully. To download and reuse this dataset, please refer to instructions on https://doi.org/10.25542/BC41-IY5R. Related Publication: Ng, Hwee Tou, & Wu, Siew Mei, & Briscoe, Ted, & Hadiwinoto, Christian, & Susanto, Raymond Hendy, & Bryant, Christopher (2014). The CoNLL-2014 Shared Task on Grammatical Error Correction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task (CoNLL-2014 Shared Task). Baltimore, Maryland.

The survey set out to explore the following questions: Which are the influential Singapore stories and why, and who do these stories resonate with? Looking ahead, which types of narratives will inspire current and future generations of Singaporeans? It was conducted in 2014. To request for a dataset to be sent to you by e-mail, you must complete the Online Form and agree to comply with the Data Use Agreement. For more information, please visit Institute of Policy Studies (IPS) Public Data Sharing Platform.