[PDF] Top 20 Learning Connective based Word Representations for Implicit Discourse Relation Identification
Has 10000 "Learning Connective based Word Representations for Implicit Discourse Relation Identification" found on our website. Below are the top 20 most common "Learning Connective based Word Representations for Implicit Discourse Relation Identification".
Learning Connective based Word Representations for Implicit Discourse Relation Identification
... word representations that live in a relatively low- dimensional space, so as to make learning a classifi- cation function over that space ...of discourse connectives, they neverthe- less form ... See full document
11
Identifying Causal Relations Using Parallel Wikipedia Articles
... into discourse semantics over the past few years. One theory of discourse structure is represented in the PDTB (Prasad et al, ...sents discourse relationships as connectives be- tween two ...that ... See full document
10
Implicit Discourse Relation Identification for Open domain Dialogues
... course relation identification models on dialogue data and our proposed features, we build on the Deep Enhanced Representation (DER) model of Bai and Zhao (2018) 7 , which demonstrated its ef- ficiency by ... See full document
7
Combining Natural and Artificial Examples to Improve Implicit Discourse Relation Identification
... a word form as a discourse ...wrong relation or that do not involve any discourse relation at ...a connective can make the discourse ackward or even incoherent (Asher and ... See full document
12
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
... Implicit discourse relation classification is one of the most difficult steps in discourse ...coherence relation must be inferred based on the content of the discourse ... See full document
12
Bilingually constrained Synthetic Data for Implicit Discourse Relation Recognition
... explicit discourse data. Zhou et al. (2010) predict the absent connectives based on a language ...aggregate word-pair features that are collected around the same connectives, which can effectively ... See full document
7
Leveraging Synthetic Discourse Data via Multi task Learning for Implicit Discourse Relation Recognition
... held implicit discourse relations (Sporleder and Lascarides, ...of implicit discourse relation recognition is the key to im- proving end-to-end discourse parser ...pattern- ... See full document
10
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
... Penn Discourse Treebank (PDTB) We use the Penn Discourse Treebank ...the implicit discourse relation ...and implicit discourse relations based on one mil- lion ... See full document
12
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
... predicting connective words as features; Park and Cardie (2012) propose a locally- optimal feature set and further identify factors for feature extraction that can have a major impact per- formance, including ... See full document
8
Implicit Discourse Relation Recognition by Selecting Typical Training Examples
... it discourse relation c lassifiers based on the training data collected (We llner, Pustejovsky and Havasi, 2006; Pitler, Louis and Nenkova, 2009; Lin, Kan and Ng 2009; Wang, Su and Tan, ...as ... See full document
16
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
... joint learning of implicit and explicit relations (Joint Learning) against learning implicit relations only (Implicit ...adding word pair features improves performance on ... See full document
11
Improving Implicit Discourse Relation Recognition with Discourse specific Word Embeddings
... DSWE based on explicit data by per- forming connective ...The connective classification task predicts which discourse con- nective is suitable for combining two given argu- ...to ... See full document
6
Comparing Word Representations for Implicit Discourse Relation Classification
... of word representations and vector composition meth- ...well-known word embeddings, namely Collobert and We- ston (Collobert and Weston, 2008), hierarchical log-bilinear model (Mnih and Hinton, 2007) ... See full document
11
Zero shot transfer for implicit discourse relation classification
... In the current paper we have presented the (to the best of our knowledge) first study of zero-shot learning in the implicit discourse relation classifi- cation task. Our method does not ... See full document
6
Crowdsourcing Discourse Relation Annotations by a Two Step Connective Insertion Task
... The perspective of being able to crowd-source coherence relations bears the promise of ac- quiring annotations for new texts quickly, which could then increase the size and vari- ety of discourse-annotated ... See full document
10
Adapting Event Embedding for Implicit Discourse Relation Recognition
... the learning curve of the implicit classifier for all input types and architec- ...the word pair classifier with an accuracy of ...using word embedding overfits quickly, as the neural network ... See full document
7
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
... contains discourse annotations over 2,312 Wall Street Jour- nal articles, and is organized in different ...each relation, we randomly ex- tracted the same number of positive and negative instances as ... See full document
6
Co learning of Word Representations and Morpheme Representations
... discrete word representations are often adopted, such as the 1-of-v representations, where v is the size of the entire vocabulary and each word in the vocabulary is represented as a long ... See full document
10
Learning Topic Sensitive Word Representations
... sensitive representations per ...per word making it difficult to scale to large ...over word senses can be approxi- mated by distributions over topics without assum- ing these concepts to be ... See full document
7
Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
... Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit ...for implicit rela- tions are lacking, and exploit domain adapta- tion from ... See full document
10
Related subjects