KL Divergence

In this paper, we will compare and evaluate the effectiveness of different statistical methods in the task of cross-document coreference resolution. We created entity models for different test sets and …...

In this study, we consider a summariza- tion method using the document level sim- ilarity based on embeddings, or distributed representations of words, where we as- sume that an embedding …...

AbstractAccording to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors. In recent work, we showed how this hypothesis can be …...

We present an approach for tackling the tweet quantification problem in SemEval 2016. The approach is based on the creation of a co- occurrence graph per sentiment from the train- …...

AbstractThis work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional …...

AbstractTraditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network …...

AbstractDeep neural networks have made great success on video captioning in supervised learning setting. However, annotating videos with descriptions is very expensive and time-consuming. If the video captioning algorithm can …...

AbstractWe give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. …...

AbstractThe widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they …...