Proceedings of EMNLP

By Shay B. Cohen. 2007

126 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tushar Khot
2. Daniel Khashabi
3. Peter Clark
4. Ashish Sabharwal
5. Zhiyuan Liu
6. Wenpeng Yin
7. Xipeng Qiu
8. Yi Tay

Citations in the corpus

• Recurrent Neural Network for Text Classification with Multi-Task Learning (2016, methodology): “, 2014], recursive neural network (RecNN) [Socher et al., 2012; 2013] and convolutional neural network (CNN) [Collobert et al.”
• Question Answering via Integer Programming over Semi-Structured Knowledge (2016, methodology): “…the capital of France?”) using document collections or databases (e.g., Freebase [Bollacker et al. , 2008], NELL [Carlson et al. , 2010]), for example [Brill et al. , 2002; Fader et al. , 2014; Ferrucci et al. , 2010; Ko et al. , 2007; Yih et al. , 2014; Yao and Van Durme, 2014; Zou et al. , 2014].”
• Iterative Entity Alignment via Joint Knowledge Embeddings (2017, methodology): “Following [Lin et al., 2015], R(p|h, t) indicates the reliability of the path p given the entity pair (h, t).”
• Joint Learning of Character and Word Embeddings (2015, methodology): “Following the idea of online nonparametric clustering algorithm [Neelakantan et al., 2014], the number of clusters for a character is unknown, and is learned during training.”
• Representation Learning of Knowledge Graphs with Hierarchical Types (2016, methodology): “[Wang et al., 2014a; Zhong et al., 2015] encode both entities and words into a joint continuous vector space by alignment models using Wikipedia anchors, entity names or entity descriptions.”
• Attention-Based Convolutional Neural Network for Machine Comprehension (2016, background): “Representative work includes Barzilay, 2015; Sachan et al. , 2015; Wang and McAllester, 2015; Smith et al. , 2015].”
• Knowledge Representation Learning with Entities, Attributes and Relations (2016, methodology): “TransH [ Wang et al. , 2014b ] enables an entity to have distinct embeddings when involved in different relations.”
• Image-embodied Knowledge Representation Learning (2016, methodology): “To utilize rich textual information, [Wang et al., 2014a] projects both entities and words into a joint vector space with alignment models.”