Abstract is missing.
- Inducing a lexicon of sociolinguistic variables from code-mixed textPhilippa Shoemark, James Kirby, Sharon Goldwater. 1-6 [doi]
- Twitter Geolocation using Knowledge-Based MethodsTaro Miyazaki, Afshin Rahimi, Trevor Cohn, Timothy Baldwin. 7-16 [doi]
- Geocoding Without Geotags: A Text-based Approach for redditKeith Harrigian. 17-27 [doi]
- Assigning people to tasks identified in email: The EPA dataset for addressee tagging for detected task intentRevanth Rameshkumar, Peter Bailey, Abhishek Jha, Chris Quirk. 28-32 [doi]
- How do you correct run-on sentences it's not as easy as it seemsJunchao Zheng, Courtney Napoles, Joel R. Tetreault. 33-38 [doi]
- A POS Tagging Model Adapted to Learner EnglishRyo Nagata, Tomoya Mizumoto, Yuta Kikuchi, Yoshifumi Kawasaki, Kotaro Funakoshi. 39-48 [doi]
- Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein DistanceSoumil Mandal, Karthick Nanmaran. 49-53 [doi]
- Robust Word Vectors: Context-Informed Embeddings for Noisy TextsValentin Malykh, Varvara Logacheva, Taras Khakhulin. 54-63 [doi]
- Paraphrase Detection on Noisy Subtitles in Six LanguagesEetu Sjöblom, Mathias Creutz, Mikko Aulamo. 64-73 [doi]
- Distantly Supervised Attribute Detection from ReviewsLisheng Fu, Pablo Barrio 0002. 74-78 [doi]
- Using Wikipedia Edits in Low Resource Grammatical Error CorrectionAdriane Boyd. 79-84 [doi]
- Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational TextsKemal Kurniawan, Samuel Louvan. 85-92 [doi]
- Orthogonal Matching Pursuit for Text ClassificationKonstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis. 93-103 [doi]
- Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical DataR. Andrew Kreek, Emilia Apostolova. 104-109 [doi]
- Detecting Code-Switching between Turkish-English Language PairZeynep Yirmibesoglu, Gülsen Eryigit. 110-115 [doi]
- Language Identification in Code-Mixed Data using Multichannel Neural Networks and Context CaptureSoumil Mandal, Anil Kumar Singh. 116-120 [doi]
- Modeling Student Response Times: Towards Efficient One-on-one Tutoring DialoguesLuciana Benotti, Jayadev Bhaskaran, Sigtryggur Kjartansson, David Lang. 121-131 [doi]
- Content Extraction and Lexical Analysis from Customer-Agent InteractionsSergiu Nisioi, Anca Bucur, Liviu P. Dinu. 132-136 [doi]
- Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, MetadataSteven Xu, Andrew Bennett, Doris Hoogeveen, Jey Han Lau, Timothy Baldwin. 137-147 [doi]
- Word-like character n-gram embeddingGeewook Kim, Kazuki Fukui, Hidetoshi Shimodaira. 148-152 [doi]
- Classification of Tweets about Reported Events using Neural NetworksKiminobu Makino, Yuka Takei, Taro Miyazaki, Jun Goto. 153-163 [doi]
- Learning to Define Terms in the Software DomainVidhisha Balachandran, Dheeraj Rajagopal, Rose Catherine Kanjirathinkal, William W. Cohen. 164-172 [doi]
- FrameIt: Ontology Discovery for Noisy User-Generated TextDan Iter, Alon Y. Halevy, Wang Chiew Tan. 173-183 [doi]
- Using Author Embeddings to Improve Tweet Stance ClassificationAdrian Benton, Mark Dredze. 184-194 [doi]
- Low-resource named entity recognition via multi-source projection: Not quite there yet?Jan Vium Enghoff, Søren Harrison, Zeljko Agic. 195-201 [doi]
- A Case Study on Learning a Unified Encoder of RelationsLisheng Fu, Bonan Min, Thien Huu Nguyen, Ralph Grishman. 202-207 [doi]
- Convolutions Are All You Need (For Classifying Character Sequences)Zach Wood-Doughty, Nicholas Andrews, Mark Dredze. 208-213 [doi]
- Step or Not: Discriminator for The Real Instructions in User-generated RecipesShintaro Inuzuka, Takahiko Ito, Jun Harashima. 214 [doi]
- Combining Human and Machine Transcriptions on the Zooniverse PlatformDaniel Hanson, Andrea Simenstad. 215-216 [doi]