[PDF] Top 20 Rationale-based Neural Networks for Justifiable Relation Extraction.

... Figure 3.1 Model Structure with sample input sentence. Entities are given from the dataset. The Generator selects candidate rationales, and the Selector enu- merates all possible combinations of candidates with entities ... See full document

... deep neural network has proven its ability to learn task-specific representation auto- matically, so that avoiding error propagation suffered by traditional feature-based ...many neural network ... See full document

... to relation extrac- tion by encoding dependency trees, and De Cao et ...novel neural architecture to generate a graph based on the textual input and dynamically update the relationship during the ... See full document

... Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang and Rajiv Ramnath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... See full document

... End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appro- priate semantic relation for each pair of ...joint neural network ... See full document

... our neural models outperformed the state-of- the-art THYME system’s ...tag- based representation (Table 1(3)), than adding time class information to the original token sequence (Table 1(7)) or adding a ... See full document

... These methods have been shown to be effec- tive for relation extraction. However, their per- formance depends strongly on the quality of the designed features. Most existing studies have con- centrated on ... See full document

... deep neural networks, many researchers have concentrated on using deep networks to learn ...primarily based on learning a distributed representation for each word, which is also called a word ... See full document

... many neural network-based models have been proposed to avoid feature ...convolutional neural networks (CNN) with max-pooling to find the relation be- tween two given ...the ... See full document

... We evaluate our models using the BC5CDR cor- pus (Li et al., 2016a) which is the benchmark dataset for the chemical-induced disease (CID) re- lation extraction task (Wei et al., 2015, 2016). 3 The corpus consists ... See full document

... in relation extraction ...in relation extraction (Riedel et ...label relation extraction. Those models are based on manually designed ...in relation ... See full document

... the relation extraction litera- ture has been dominated by two methods, distin- guished by the nature of the relation representa- tion: the feature-based method (Kambhatla, 2004; Boschee et ... See full document

... supervised relation extraction has been widely used to find novel relational facts from ...for relation extrac- tion. In this model, we employ convolu- tional neural networks to embed ... See full document

... for relation extraction (Zhang and Wang, 2015), uniquely in- dicate the event positions to LSTM, such that the final output of LSTM can be used as a represen- tation of those events and their ... See full document

... tion neural models such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) to mark the positions of the entities and achieved bet- ter performance ... See full document

... KBs, relation extraction from plain text has attracted many research in- ...terests. Relation extraction typically classifies each entity pair into various relation types ac- cording to ... See full document

... We compare our proposed model 3 with three ex- isting models including CNN (the state-of-the- art supervised approach by Lin et al. (2016)), MiniE (the state-of-the-art unsupervised approach by Gashteovski et al. ... See full document

... Table 2: Accuracies on the four RoB datasets. Uni-SVM: unigram SVM, Bi-SVM: Bigram SVM, RA-SVM: Rationale-augmented SVM (Zaidan et al., 2007), MT-SVM: a multi-task SVM model specifically designed for the RoB task, ... See full document

... Compared with the simple SCNN network and Alexnet network, the above experimental results show that combining two algorithms and LSTM is often closer to the expert collocation rules. Because the association rules of ... See full document

... tem competition) in 2005 and 2006. In addition, Banch and Li. proposed a model based on the vec- tor space model (Banchs and Li, 2012) and Nio et al. constructed a dialogue system that uses movie scripts and ... See full document