fendaq / nrepapers2018

This repo covers almost all the papers (35) related to Neural Relation Extraction in ACL, EMNLP, COLING, NAACL, AAAI, IJCAI in 2018.

Relation Extraction in 2018

This repo covers almost all the papers (35) related to Neural Relation Extraction in ACL, EMNLP, COLING, NAACL, AAAI, IJCAI in 2018.

Use tags to search papers you like.

tags: Multi-lingual NRE | DSRE | new task | NRC | Joint extraction of both entity and relation | bootstrapping RE | few shot | GCN | capsule network | document-level RE | Multi-label | Denoise | rule | KG

DSRE: Distant Supervision Relation Extraction

NRE: Neural Relation Extraction

NRC: Neural Relation Classification

GCN: Graph Convolution Network

KG: Knowledge Graph Inforamtion

Note that DSRE is a big track in RE, including a denoising preprocessing step | bootstrapping RE | Learning in the noise .etc

COLING 2018

1. Adversarial Multi-lingual Neural Relation Extraction Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu and Maosong Sun. COLING 2018 paper code

   Multi-lingual NRE

   Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns.

2. Cooperative Denoising for Distantly Supervised Relation Extraction Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan and Ying Shen. COLING 2018 paper

   DSRE: Learning in the noise | Denoise | KG | rule

   we propose a novel neural relation extraction framework with bi-directional knowledge distillation to cooperatively use different information sources and alleviate the noisy label problem in distantly supervised relation extraction. Specifically, a soft rule w.r.t entity type is appled to correct the predicted probability.

3. Exploratory Neural Relation Classification for Domain Knowledge Acquisition Yan Fan, Chengyu Wang and Xiaofeng He. COLING 2018 paper

   Exploratory Relation Classification (ERC): NRC + new task!

   In this paper, we propose the task of ERC to address the problem of domain-specific knowledge acquisition. We propose a DSNN model to address the task, consisting of three modules, an integrated base neural network for relation classification, a similarity-based clustering algorithm ssCRP to generate new relations and constrained relation prediction process with the purpose of populating new relations.

4. Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences Shiou Tian Hsu, Mandar Chaudhary and Nagiza Samatova. COLING 2018 paper

   NRC + rationale interpretability NRC + new task!

   In this paper, we have proposed an improved rationale-based model for entity relation classification. In our model, besides context word information, we also moderate rationale generation with multiple heuristics computed from different text level features.

5. Neural Relation Classification with Text Descriptions Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu and Xiaobo Liang. COLING 2018 paper

   NRC

   In this paper, we propose DesRC, a new neural relation classification method which integrates entities text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the ”intra-sentence” aspect and the ”cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification performance.

6. Adversarial Feature Adaptation for Cross-lingual Relation Classification Bowei Zou, Zengzhuang Xu, Yu Hong and Guodong Zhou. COLING 2018 paper

   Multi-lingual NRE

   In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.

ACL 2018

1. Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism. Xiangrong Zeng, Daojian Zeng, Shizhu He, Kang Liu, Jun Zhao. ACL 2018 paper

   Joint extraction of both entity and relation

   In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes.

2. Adaptive Scaling for Sparse Detection in Information Extraction. Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun. ACL 2018 paper

   In this paper, we propose adaptive scaling, an algorithm which can handle the positive sparsity problem and directly optimize over F-measure via dynamic costsensitive learning.

3. Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. Pengda Qin, Weiran XU, William Yang Wang. ACL 2018 paper

   DSRE | Denoise: a denoising preprocessing step

   We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples.

4. DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction. Pengda Qin, Weiran XU, William Yang Wang. ACL 2018 paper

   DSRE | Denoise: a denoising preprocessing step

   Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification.

5. A Walk-based Model on Entity Graphs for Relation Extraction. Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou. ACL 2018 paper

   NRC

   We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure.

6. Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction. Van-Thuy Phi, Joan Santoso, Masashi Shimbo, Yuji Matsumoto. ACL 2018 paper

   DSRE | Denoise | bootstrapping RE

   This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction.

AAAI 2018

1. Large Scaled Relation Extraction with Reinforcement Learning Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao AAAI 2018 paper

   DSRE: sentence level rather than bag level

   In this paper, we learn the relation extractor with reinforcement learning method on the distant supervised dataset. The bag relation is used as the distant supervision which guide the training of relation extractor. We also apply the relation extractor to help bag relation extraction

2. SEE: Syntax-aware Entity Embedding for Neural Relation Extraction Zhengqiu He*, Wenliang CHEN, Meishan Zhang, Zhenghua Li, Wei Zhang, Min Zhang AAAI 2018 paper

   DSRE

   we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification.

3. Reinforcement Learning for Relation Classification from Noisy Data Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu AAAI2018 paper

   DSRE: sentence level rather than bag level

   we propose a novel model for relation classification at the sentence level from noisy data. The model has two modules: an instance selector and a relation classifier. The instance selector chooses high-quality sentences with learning and feeds the selected sentences into the relation classifier, and the relation classifier makes sentencelevel prediction and provides rewards to the instance selector. The two modules are trained jointly to optimize the instance selection and relation classification processes.

IJCAI 2018

1. Joint Extraction of Entities and Relations Based on a Novel Graph Scheme Shaolei Wang, Yue Zhang, Wanxiang Che, Ting Liu IJCAI 2018 paper

   Joint extraction of both entity and relation

   In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations.

2. Ensemble Neural Relation Extraction with Adaptive Boosting Dongdong Yang, Senzhang Wang, Zhoujun Li IJCAI 2018 paper

   DSRE

   we proposed to integrate attention-based LSTMs with adaptive boosting model for relation extraction.

3. Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction Sen Su, Ningning Jia, Xiang Cheng, Shuguang Zhu, Ruiping Li IJCAI 2018 paper

   DSRE | Multi-label

   we present a simple yet effective encoderdecoder model for distant supervised relation extraction. Given the sentence bag of an entity pair as input, the CNN encoder extracts sentence features and merge them into a bag representation. While the LSTM decoder leverages the dependencies among the relations by predicting them in a sequential manner. To enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information contained in a sentence bag for its relations, which are used to determine relation orders during training to let the model predict relations in a descending order of their amounts of information.

NAACL 2018

1. GLOBAL RELATION EMBEDDING FOR RELATION EXTRACTION Yu Su, Honglei Liu, Semih Yavuz, Izzeddin Gur, Huan Sun and Xifeng Yan NAACL 2018 paper

   DSRE

   we propose to embed textual relations with global statistics of relations, i.e., the cooccurrence statistics of textual and knowledge base relations collected from the entire corpus.

2. JOINT BOOTSTRAPPING MACHINES FOR RELATION EXTRACTION Pankaj Gupta and Hinrich Schütze NAACL 2018 paper

   bootstrapping RE

   We have proposed a Joint Bootstrapping Machine for relation extraction (BREJ) that takes advantage of both entity-pair-centered and template-centered approaches. We have demonstrated that the joint approach scales up positive instances that boosts the confidence of NNLC extractors and improves recall.

3. SIMULTANEOUSLY SELF-ATTENDING TO ALL MENTIONS FOR FULL-ABSTRACT BIOLOGICAL RELATION EXTRACTION Patrick Verga, Emma Strubell and Andrew McCallum NAACL 2018 paper

   document-level RE + new task

   We present a bi-affine relation attention network that simultaneously scores all mention pairs within a document.

   a new, large and high-quality dataset introduced in this work.

4. STRUCTURE REGULARIZED NEURAL NETWORK FOR ENTITY RELATION CLASSIFICATION FOR CHINESE LITERATURE TEXT Ji Wen, Xuancheng Ren, Xu Sun and Qi Su NAACL 2018 paper

   Chinese NRC + new task

   We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model.

EMNLP 2018

1. Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction Jinhua Du, Jingguang Han, Andy Way and Dadong Wan EMNLP 2018 paper

   DSRE

   This paper has proposed a multi-level structured self-attention mechanism for distantly supervised RE. In this framework, the traditional 1-D wordlevel and sentence-level attentions are extended to 2-D structured matrices which can learn different aspects of a sentence, and different informative instances.

2. Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun and Peng Li EMNLP 2018

   paper not released

   ppt

3. Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning Tianyi Liu, Xinsong Zhang, Wanhao Zhou and Weijia Jia EMNLP 2018 paper

   DSRE | Denoise

   We first build Sub-Tree Parse (STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the subtree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning.

4. N-ary Relation Extraction using Graph State LSTM Linfeng Song, Yue Zhang, Zhiguo Wang and Daniel Gildea EMNLP 2018 paper

   N-ary relation extraction | GCN related

   We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing.

5. RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya and Partha Talukdar EMNLP 2018 paper

   DSRE | GCN | KG

   utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available.

6. Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou and Huajun Chen EMNLP 2018

   paper

   DSRE | KGE

   zhihu

   What if TransE + PCNN? What if we replace entity mentions with entity types? Learn Label-Free DSRE!

7. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction Yuhao Zhang, Peng Qi and Christopher D. Manning EMNLP 2018

   paper

   GCN

   We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold.

8. Extracting Entities and Relations with Joint Minimum Risk Training sun changzhi, Yuanbin Wu and Man Lan

   EMNLP 2018

   paper

   Joint extraction of both entity and relation

   This paper purpose a fine-tune trick to further boost the performance of the joint extraction model. Different from exist methods, the minimum risk training is more suitable with regard to the F1 evaluation. Moreover, as the empirical risk is intractable, they use a sampling algrithm for approximation.

9. Large-scale Exploration of Neural Relation Classification Architectures Hoang-Quynh Le, Duy-Cat Can, Sinh T. Vu, Thanh Hai Dang, Mohammad Taher Pilehvar and Nigel Collier

   EMNLP 2018

   paper not released

10. Adversarial training for multi-context joint entity and relation extraction Giannis Bekoulis, Johannes Deleu, Thomas Demeester and Chris Develder

    EMNLP 2018

    paper

    Joint extraction of both entity and relation

    (related paper: Joint entity recognition and relation extraction as a multi-head selection problem)

    we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets.

11. FewRel: A Large-Scale Supervised Few-shot Relation Classification Dataset with State-of-the-Art Evaluation Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu and Maosong Sun

    EMNLP 2018

    few-shot

    paper

    ppt

    leaderboard

    few shot learning is a big problem in DSRE which is far from solved! This paper introduces a new competition!

12. Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao and Heyan Huang

    EMNLP 2018

    paper

    NRC | cross-genre relation extraction

    we design a genre-separation network, which applies two encoders, one genre independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre.

13. Attention-Based Capsule Network with Dynamic Routing for Relation Extraction Ningyu Zhang, Shumin Deng, Huajun Chen, Zhanling Sun, Yiyi Zhang and Xiaoqian Li

    EMNLP 2018

    paper

    capsule network | Multi-label

    Capsule Network is good at multi-label classification in RE in NLP!