Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
By Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao. AAAI Conference on Artificial Intelligence, 2017
386 citations recorded by the source corpus.
Distant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the attention module. We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/b8da823ad81e3b8e5b80d82f86129fdb1d9132e7
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In Artificial Intelligence
Citations in the corpus
- Reinforcement Learning for Relation Classification From Noisy Data (2018, methodology): “To address the issue of noisy labeling, previous studies adopt multi-instance learning to consider the noises of instances (Riedel, Yao, and McCallum 2010; Hoffmann et al. 2011; Surdeanu et al. 2012; Zeng et al. 2015; Lin et al. 2016; Ji et al. 2017).”
- Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (2018, methodology): “We evaluate our models on the New York Times (NYT) dataset developed by Riedel et al. (2010), which is widely used in recent works (Lin et al., 2016; Zeng et al., 2017; Ji et al., 2017; Han et al., 2018; Liu et al., 2017; Wu et al., 2017; Huang and Wang, 2017; Feng et al., 2018; Zeng et al., 2018).”
- Attention, please! A survey of neural attention models in deep learning (2021, background): “(2014) proposed a similar approach, however directly focused on CNNs, as shown in Fig.”
- Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021, background): “…and utilizing various deep neural networks to discover the relations be-tween entities given contexts, including: (1) Convolutional neural networks (CNNs) (Zeng et al., 2014, 2015; Nguyen and Grishman, 2015; Lin et al., 2016; Ji et al., 2017) can effectively extract local textual features.”
- Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (2019, methodology): “Different from conventional methods (Lin et al., 2016; Ji et al., 2017) where a unified bag representation was derived for relation classification, our method calculates bag representations b ik for bag b i on the condition of all possible relations as where k ∈ { 1 , 2 , ..., h } is the relation…”
- DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (2018, background): “Lin et al. (2016) calculate a series of soft attention weights for all sentences of one entity pair and the incorrect sentences can be down-weighted; Base on the same idea, Ji et al. (2017) bring the useful entity information into the calculation of the attention weights.”
- Enriching Pre-trained Language Model with Entity Information for Relation Classification (2019, background): “tity typing for relation classification. There are some related work on the relation extraction based on distant supervision, for example, (Mintz et al., 2009; Hoffmann et al., 2011; Lin et al., 2016; Ji et al., 2017; Wu et al., 2019). The difference between relation classification on regular data and”
- A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “[195] combine entity descriptions to enhance the MIML CNN.”
- More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, methodology): “(2) Incorporating extra context information to denoise DS data has also been explored, such as incorporating KGs as external information to guide instance selection (Ji et al., 2017; Han et al., 2018b; Zhang et al., 2019a; Qu et al., 2019) and adopting multi-lingual corpora for the information consi”
- Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (2019, background): “To further improve the performance, some studies incorporate external information (Zeng et al., 2017; Ji et al., 2017; Han et al., 2018a) and advanced training strategies (Ye et al.”
- Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification (2019, background): “…reduce noise caused by DS (Verga et al. 2016; Verga and McCallum 2016; Liu et al. 2017; Huang and Wang 2017) and introduce more extra information (Ji et al. 2017; Han, Liu, and Sun 2018), including some sophisticated mechanisms, such as reinforcement learning (Feng et al. 2018; Zeng et al. 2018)…”
- Detecting Absurd Conversations from Intelligent Assistant Logs by Exploiting User Feedback Utterances (2018, methodology): “The use of feedback utterances to automatically acquire the labeled data for the conversation classifier in our method was inspired by distant supervision [6, 16].”
- FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, methodology): “We evaluate the model proposed by Lin et al. (2016), which is followed by the recent state-of-the-art methods (Zeng et al., 2017; Ji et al., 2017; Huang and Wang, 2017; Wu et al., 2017; Liu et al., 2017; Feng et al., 2018; Zeng et al., 2018), on the benchmark dataset NYT-10 NYT-10 ( Riedel We…”