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...
Identifying the type of relationship between words provides a deeper insight into the history of a language and allows a better characterization of language relatedness. In this p...
This paper reports on and demonstrates META-SHARE/QT21, a prototype implementation of a data sharing and annotation service platform, which was based on the META-SHARE infrastruct...
Stelios Piperidis, Dimitrios Galanis, Juli Bakagia...
Stubs on Wikipedia often lack comprehensive information. The huge cost of editing Wikipedia and the presence of only a limited number of active contributors curb the consistent gr...
We propose a novel semantic parsing framework for question answering using a knowledge base. We define a query graph that resembles subgraphs of the knowledge base and can be dir...
Methods for name matching, an important component to support downstream tasks such as entity linking and entity clustering, have focused on alphabetic languages, primarily English...
We propose a neural network model for scalable generative transition-based dependency parsing. A probability distribution over both sentences and transition sequences is parameter...
This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the ...