In natural language understanding (NLU), a user utterance can be labeled differently depending on the domain or application (e.g., weather vs. calendar). Standard domain adaptatio...
Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, Minwoo...
Many NLP tools for English and German are based on manually annotated articles from the Wall Street Journal and Frankfurter Rundschau. The average readers of these two newspapers ...
Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These tradition...
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, Jun ...
Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the web. This paper is concerned with the problem of question retrieval. ...
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation ...
Compared with carefully edited prose, the language of social media is informal in the extreme. The application of NLP techniques in this context may require a better understanding...
Luchen Tan, Haotian Zhang, Charles L. A. Clarke, M...
Most previous work of text normalization on informal text made a strong assumption that the system has already known which tokens are non-standard words (NSW) and thus need normal...
Meaning of a word varies from one domain to another. Despite this important domain dependence in word semantics, existing word representation learning methods are bound to a singl...
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a ...
Aditya Joshi, Abhijit Mishra, Balamurali A. R, Pus...
We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single...