We present a novel approach to the selective annotation of large corpora through the use of machine learning. Linguistic search engines used to locate potential instances of an in...
In this paper, we present a model for improved discriminative semantic parsing. The model addresses an important limitation associated with our previous stateof-the-art discrimina...
People create morphs, a special type of fake alternative names, to achieve certain communication goals such as expressing strong sentiment or evading censors. For example, “Blac...
Simultaneous translation is a method to reduce the latency of communication through machine translation (MT) by dividing the input into short segments before performing translatio...
Yusuke Oda, Graham Neubig, Sakriani Sakti, Tomoki ...
Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutiona...
Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, ...
We consider the task of identifying and labeling the semantic arguments of a predicate that evokes a FrameNet frame. This task is challenging because there are only a few thousand...
Meghana Kshirsagar, Sam Thomson, Nathan Schneider,...
This work develops a new statistical understanding of word embeddings induced from transformed count data. Using the class of hidden Markov models (HMMs) underlying Brown clusteri...
In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful ...
Topic Model such as Latent Dirichlet Allocation(LDA) makes assumption that topic assignment of different words are conditionally independent. In this paper, we propose a new model...
We study the problem of summarizing DAG-structured topic hierarchies over a given set of documents. Example applications include automatically generating Wikipedia disambiguation ...
Ramakrishna Bairi, Rishabh K. Iyer, Ganesh Ramakri...