• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 32 matches

• 2016
• 
  Fahad R. Albogamy, Allan Ramsay:
  Unsupervised Stemmer for Arabic Tweets. NUT@COLING 2016: 78-84
• 
  Héctor Martínez Alonso, Djamé Seddah, Benoît Sagot:
  From Noisy Questions to Minecraft Texts: Annotation Challenges in Extreme Syntax Scenario. NUT@COLING 2016: 13-23
• 
  Anietie Andy, Satoshi Sekine, Mugizi Rwebangira, Mark Dredze:
  Name Variation in Community Question Answering Systems. NUT@COLING 2016: 51-60
• 
  Yasunobu Asakura, Masatsugu Hangyo, Mamoru Komachi:
  Disaster Analysis using User-Generated Weather Report. NUT@COLING 2016: 24-32
• 
  Thales Felipe Costa Bertaglia, Maria das Graças Volpe Nunes:
  Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization. NUT@COLING 2016: 112-120
• 
  Belainine Billal, Alexsandro Fonseca, Fatiha Sadat:
  Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets. NUT@COLING 2016: 102-111
• 
  Florian Boudin, Hugo Mougard, Damien Cram:
  How Document Pre-processing affects Keyphrase Extraction Performance. NUT@COLING 2016: 121-128
• 
  Ming-Wei Chang:
  From Entity Linking to Question Answering - Recent Progress on Semantic Grounding Tasks. NUT@COLING 2016: 2
• 
  Lianhua Chi, Kwan Hui Lim, Nebula Alam, Christopher J. Butler:
  Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features. NUT@COLING 2016: 227-234
• 
  Fabrice Dugas, Eric Nichols:
  DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets. NUT@COLING 2016: 178-187
• 
  Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, Sophia Ananiadou:
  Learning to recognise named entities in tweets by exploiting weakly labelled data. NUT@COLING 2016: 153-163
• 
  Michel Naim Gerguis, Cherif R. Salama, M. Watheq El-Kharashi:
  ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition. NUT@COLING 2016: 188-196
• 
  Bo Han, Afshin Rahimi, Leon Derczynski, Timothy Baldwin:
  Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text. NUT@COLING 2016: 213-217
• 
  Taishi Ikeda, Hiroyuki Shindo, Yuji Matsumoto:
  Japanese Text Normalization with Encoder-Decoder Model. NUT@COLING 2016: 129-137
• 
  Ajit Jain, Girish Kasiviswanathan, Ruihong Huang:
  Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators. NUT@COLING 2016: 70-77
• 
  Gaya Jayasinghe, Brian Jin, James McHugh, Bella Robinson, Stephen Wan:
  CSIRO Data61 at the WNUT Geo Shared Task. NUT@COLING 2016: 218-226
• 
  Ngoc Tan Le, Fatma Mallek, Fatiha Sadat:
  UQAM-NTL: Named entity recognition in Twitter messages. NUT@COLING 2016: 197-202
• 
  Nut Limsopatham, Nigel Collier:
  Bidirectional LSTM for Named Entity Recognition in Twitter Messages. NUT@COLING 2016: 145-152
• 
  Nikola Ljubesic, Darja Fiser:
  Private or Corporate? Predicting User Types on Twitter. NUT@COLING 2016: 4-12
• 
  Shubhanshu Mishra, Jana Diesner:
  Semi-supervised Named Entity Recognition in noisy-text. NUT@COLING 2016: 203-212
• 
  Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma:
  A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter. NUT@COLING 2016: 235-239
• 
  Julie Pain, Jessie Levacher, Adam Quinquenel, Anja Belz:
  Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance. NUT@COLING 2016: 94-101
• 
  Ioannis Partalas, Cédric Lopez, Nadia Derbas, Ruslan Kalitvianski:
  Learning to Search for Recognizing Named Entities in Twitter. NUT@COLING 2016: 171-177
• 
  Barbara Plank:
  Processing non-canonical or noisy text: fortuitous data to the rescue. NUT@COLING 2016: 1
• 
  Uwe D. Reichel, Piroska Lendvai:
  Veracity Computing from Lexical Cues and Perceived Certainty Trends. NUT@COLING 2016: 33-42
• 
  Utpal Kumar Sikdar, Björn Gambäck:
  Feature-Rich Twitter Named Entity Recognition and Classification. NUT@COLING 2016: 164-170
• 
  Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, Wei Xu:
  Results of the WNUT16 Named Entity Recognition Shared Task. NUT@COLING 2016: 138-144
• 
  Jing Su, Derek Greene, Oisín Boydell:
  Topic Stability over Noisy Sources. NUT@COLING 2016: 85-93
• 
  Kentaro Torisawa:
  DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets. NUT@COLING 2016: 3
• 
  Wei-Chung Wang, Hung-Chen Chen, Zhi-Kai Ji, Hui-I Hsiao, Yu-Shian Chiu, Lun-Wei Ku:
  Whose Nickname is This? Recognizing Politicians from Their Aliases. NUT@COLING 2016: 61-69

skipping 2 more matches