| Challenge: | Relation classification is the task to predict semantic relations between pairs of entities in a given text. |
| Approach: | They propose to extract relations between entities in Chinese text using a long-term memory network. |
| Outcome: | The proposed system achieves state-of-the-art F-measure on ACE 2005 corpus . it predicts relations between head entity e h and tail entity t from sentence . |
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Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
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| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
| Approach: | They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction. |
| Outcome: | The proposed model outperforms existing models on three RE benchmark datasets. |
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
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| Challenge: | Existing approaches to extract relationship between entities in sentences suffer from missing or redundant information. |
| Approach: | They propose a deep neural model that combines the advantages of the two approaches to extract the relationship between two entities in a sentence. |
| Outcome: | The proposed model outperforms baseline models on the SemEval-2010 dataset. |
A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
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| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
| Approach: | They propose a graph-based neural network model that treats multiple pairs in a sentence simultaneously and considers interactions among them. |
| Outcome: | The proposed model performs comparable to the state-of-the-art systems on the ACE 2005 dataset without external tools. |
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text (N18-2)
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| Challenge: | Existing methods for relation classification have been used in natural language processing. |
| Approach: | They propose a relation classification task for Chinese literature text using a new dataset. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on Chinese literature text. |
Exploratory Neural Relation Classification for Domain Knowledge Acquisition (C18-1)
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| Challenge: | Existing methods for relation classification are limited and lack of low-frequency relations in specific domains. |
| Approach: | They propose a method to learn a classifier on pre-defined relations and discover new relations expressed in texts. |
| Outcome: | The proposed method can classify entities into a finite set of relations and discover relations with high precision and recall. |
Dependency-Guided LSTM-CRF for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the most important and fundamental tasks in natural language processing (NLP). |
| Approach: | They propose a dependency-guided model to encode dependency trees and capture their properties for named entity recognition. |
| Outcome: | The proposed model improves named entity recognition performance on standard datasets. |
N-ary Relation Extraction using Graph-State LSTM (D18-1)
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| Challenge: | Existing methods for cross-sentence relation extraction split the input graph into two DAGs, but important information can be lost in the splitting procedure. |
| Approach: | They propose a graph-state LSTM model which uses a parallel state to model each word, recurrently enriching state values via message passing. |
| Outcome: | The proposed model keeps the original graph structure, and speeds up computation by allowing more parallelization. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
Large-scale Exploration of Neural Relation Classification Architectures (D18-1)
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| Challenge: | Existing studies on relation classification have been limited to a very narrow range of datasets, making comparisons between approaches difficult. |
| Approach: | They propose a multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and provides direct insights into the challenges faced by language models on relation classification. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |