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
DataStoriesAbstractThis paper built two sentiment analysis systems based on LSTM with two kinds of attention mechanism on top of word embeddings pre-trained on a big collection of Twitter messages. Th
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
AbstractIn this paper, a novel morph-fitting procedure is proposed to injects morphological constraints generated using simple language-specific rules. Result: improves low-frequency word estimates;
wordscongruent:全等 AbstractThe paraphrase Database (PPDB) consists of a list of phrase pairs with heuristic confidence estimates. Its coverage is also necessarily incomplete. They propose models to:
IntroductionThis paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representation
名词解释antonymy:反义关系synonymy:同义关系DST:dialogue state tracking task aimimprove the vectors’ capability for judging semantic similarity result lead to a new state of the art performance on the SimLex-999 da