A dependency measure based on Jensen-ShannonThey define a dependency measure between two random variables, which is based on the Jensen-Shannon divergence. The Kullback-Leibler (KL) divergence of a d
MethologyHowNetHowNet annotates precise senses to each word, and for each sense, HowNet annotates the significance of parts and attributes represented by sememes.
AbstractThey introduce an extension to the bag-of-words model for learning words represen- tations that take into account both syntactic and semantic properties within language. IntroductionFor BOW an
AbstractContext representations are a key element in distributional models of word meaning. A recently proposed approach suggests to represent a context of a target word by a substitute vector. In thi
IntroductionMost existing work on multi-task learning attempts to divide the features of different tasks into private and shared spaces, merely based on whether parameters of some components should be
IntroductionDiscourse structure represents the organization of a text as a tree. Experimental results show that variants of our model outperform prior work on four out of five tasks considered. In thi
AbstractCompare two variants of retrofitting and a joint-learning approach. Although the word embeddings is sometimes desirable, it may in other cases be detrimental to downstream performance. For ex
Abstract and IntroductionThey introduce a new vector space representation where antonyms lie on opposite sides of a sphere: in the word vector space, synonyms have cosine similarities close to one, wh
AbstractA new learning objective that incorporates both a neural language model objective and prior knowledge from semantic resources to learn improved lexical semantic embeddings. IntroductionIn thi
AbstractThey reveal that much of the performance gains of word embeddings are due to certain system design choices, rather than the embedding algorithms themselves. Furthermore, they show that these m