📰 Workshop on Noisy User-generated Text 2016: all accepted papers

31 papers · the most cited first · ← Workshop on Noisy User-generated Text 2016

1. A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter ↗Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi et al.
2. A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine TranslationMarlies van der Wees, Arianna Bisazza, Christof Monzour summary
3. ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition. ↗Michel Naim Gerguis, Cherif Salama, M. Watheq El-Kharashi
4. Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance ↗Julie Pain, Jessie Levacher, Adam Quinquenel et al.
5. Bidirectional LSTM for Named Entity Recognition in Twitter Messages ↗Nut Limsopatham, Nigel Collier
6. CSIRO Data61 at the WNUT Geo Shared Task ↗Gaya Jayasinghe, Brian Jin, James Mchugh et al.
7. DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets ↗Kentaro Torisawa
8. DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets ↗Fabrice Dugas, Eric Nichols
9. Disaster Analysis using User-Generated Weather Report ↗Yasunobu Asakura, Masatsugu Hangyo, Mamoru Komachi
10. Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization ↗Thales Felipe Costa Bertaglia, Maria das Graças Volpe Nunes
11. Feature-Rich Twitter Named Entity Recognition and Classification ↗Utpal Kumar Sikdar, Björn Gambäck
12. From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks ↗Ming-Wei Chang
13. From Noisy Questions to Minecraft Texts: Annotation Challenges in Extreme Syntax Scenario ↗Héctor Martínez Alonso, Djamé Seddah, Benoît Sagot
14. Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features ↗Lianhua Chi, Kwan Hui Lim, Nebula Alam et al.
15. How Document Pre-processing affects Keyphrase Extraction Performance ↗Florian Boudin, Hugo Mougard, Damien Cram
16. Japanese Text Normalization with Encoder-Decoder Model ↗Taishi Ikeda, Hiroyuki Shindo, Yuji Matsumoto
17. Learning to Search for Recognizing Named Entities in Twitter ↗Ioannis Partalas, Cédric Lopez, Nadia Derbas et al.
18. Learning to recognise named entities in tweets by exploiting weakly labelled data ↗Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, Sophia Ananiadou
19. Name Variation in Community Question Answering Systems ↗Anietie Andy, Satoshi Sekine, Mugizi Rwebangira et al.
20. Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets ↗Billal Belainine, Alexsandro Fonseca, Fatiha Sadat
21. Private or Corporate? Predicting User Types on Twitter ↗Nikola Ljubešić, Darja Fišer
22. Processing non-canonical or noisy text: fortuitous data to the rescue ↗Barbara Plank
23. Results of the WNUT16 Named Entity Recognition Shared Task ↗Benjamin Strauss, Bethany Toma, Alan Ritter et al.
24. Semi-supervised Named Entity Recognition in noisy-text ↗Shubhanshu Mishra, Jana Diesner
25. Topic Stability over Noisy Sources ↗Jing Su, Derek Greene, Oisín Boydell
26. Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators ↗Ajit Jain, Girish Kasiviswanathan, Ruihong Huang
27. Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text ↗Bo Han, Afshin Rahimi, Leon Derczynski et al.
28. UQAM-NTL: Named entity recognition in Twitter messages ↗Ngoc Tan Le, Fatma Mallek, Fatiha Sadat
29. Unsupervised Stemmer for Arabic TweetsFahad Albogamy, Allan Ramsayour summary
30. Veracity Computing from Lexical Cues and Perceived Certainty Trends ↗Uwe Reichel, Piroska Lendvai
31. Whose Nickname is This? Recognizing Politicians from Their Aliases ↗Wei-Chung Wang, Hung-Chen Chen, Zhi-Kai Ji et al.

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