Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information
Abstract
:1. Introduction
- We propose a Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information (EINET). On the premise of using the pre-trained model, we introduce explicit semantic information and fully explore the implicit semantic information for joint entity and relation extraction.
- As far as we know, we are the first one to use semantic role labeling information for joint entity and relation extraction. Semantic role labeling can not only provide explicit semantic information for NER and RE, but also helps the model to enhance semantic understanding of text.
- While adopting the BERT pre-trained model, we further explore the implicit semantic information of entities and local contexts based on different Bi-LSTMs. By our separate encoding method, the different features of entities and local contexts are fully explored, so as to purposefully improve the performance of named entity recognition and relation extraction.
- We propose to integrate global semantic information and local context length representation in relation extraction to further improve the model performance.
- Our model shows strong competitiveness on three publicly available joint entity and relation extraction datasets (Conll04, SCIERC, ADE), achieving competitive experimental results.
2. Related Work
2.1. Sequence Tagging Based Method
2.2. Span-Based Method
3. Materials and Methods
3.1. Word Representation
3.1.1. Pre-Trained Model
3.1.2. Semantic Role Labeling Information
3.2. Named Entity Recognition
3.3. Relation Extraction
4. Experiment and Result Analysis
4.1. Experimental Settings
4.1.1. Datasets
4.1.2. Implementation Details
4.2. Comparison of Results on Datasets
4.3. Ablation Analysis
4.4. Visualization
4.5. Error Cases
- (1)
- Incorrect spans: A common error is the prediction of a slightly incorrect entity span, usually with one more or one less word than the ground truth. Here, “interferon alfa” should be marked as an entity but we marke “interferon” as one entity. This error occurs particularly often in domain-specific ADE and SciERC datasets.
- (2)
- Logical: Sometimes, the relationship between entities is not explicitly described in the sentence, but can be logically inferred from the context. In the case described, the “Work-For” relationship between “Robert Bernero” and “DOE” needs to be inferred from some information (“Robert Bernero, chief of waste disposal for the commission” and “the commission refers to DOE”).
- (3)
- Missing annotation: There are some cases where a correct prediction is missing in the ground truth. Here, in addition to the correct prediction (Shoshone-Bannock, Located-In, Idaho), EIENT also outputs (Hatcher, Live-In, Onondaga territory), (Hatcher, Live-In, Shoshone-Bannock) and (Hatcher, Live-In, Idaho), which are correct but unmarked.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
NLP | Natural Language Processing |
NER | Named Entity Recognition |
RE | Relation Extraction |
SRL | Semantic Role Labeling |
BERT | Bidirectional Encoder Representation from Transformers |
Bi-LSTM | Bi-directional Long Short-Term Memory |
EINET | Joint Entity and Relation Extraction Network with Enhanced Explicit and |
Implicit Semantic Information | |
OOV | Out of Vocabulary |
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Model | Entity | Relation | ||||
---|---|---|---|---|---|---|
Precision | Recall | F1 | Precision | Recall | F1 | |
Relation-Metric [32] ‡ | 84.46 | 84.67 | 84.57 | 67.97 | 58.18 | 62.68 |
Biaffine Attention [33] ‡ | - | - | 86.20 | - | - | 64.40 |
Multi-turn QA [18] † | 89.00 | 86.60 | 87.80 | 69.20 | 68.20 | 68.90 |
Multi-head + AT [16] ‡ | - | - | 83.61 | - | - | 61.95 |
Multi-head [34] ‡ | 83.75 | 84.06 | 83.90 | 63.75 | 60.43 | 62.04 |
Hierarchical Attention [35] * | - | - | 86.51 | - | - | 63.32 |
SpERT [26] † | 88.25 | 89.64 | 88.94 | 73.04 | 70.00 | 71.47 |
SpERT [26] ‡ | 85.78 | 86.84 | 86.25 | 74.75 | 71.52 | 72.87 |
MRC4ERE++ [19] * | 89.30 | 88.50 | 88.90 | 72.20 | 71.50 | 71.90 |
UMT w/ NLGQ [20] * | 88.70 | 88.80 | 88.80 | 72.90 | 71.60 | 72.20 |
UMT w/ PseudoGQ [20] * | 88.80 | 89.00 | 88.90 | 73.20 | 71.60 | 72.40 |
TriMF [28] † | 89.26 | 90.34 | 90.30 | 73.01 | 71.63 | 72.35 |
Two are better than one [36] † | - | - | 90.10 | - | - | 73.80 |
Two are better than one [36] ‡ | - | - | 86.90 | - | - | 75.80 |
EINET † | 92.43 | 90.22 | 91.31 | 77.15 | 72.78 | 74.90 |
EINET ‡ | 90.65 | 86.70 | 88.51 | 77.98 | 74.16 | 75.91 |
Model | Entity | Relation | ||||
---|---|---|---|---|---|---|
Precision | Recall | F1 | Precision | Recall | F1 | |
SciIE [17] | 67.20 | 61.50 | 64.20 | 47.60 | 33.50 | 39.30 |
DyGIE [22] | - | - | 65.20 | - | - | 41.46 |
DyGIE++ [25] | - | - | 67.50 | - | - | 48.40 |
SpERT [26] | 70.87 | 69.79 | 70.33 | 53.40 | 48.54 | 50.84 |
UNIRE [39] | 65.80 | 71.10 | 68.40 | 37.30 | 36.60 | 36.90 |
PFN [40] | - | - | 66.80 | - | - | 38.40 |
PURE [41] | - | - | 68.90 | - | - | 50.10 |
TriMF [28] | 70.18 | 70.17 | 70.17 | 52.63 | 52.32 | 52.44 |
SpERT.PL [42] | 69.82 | 71.25 | 70.53 | 51.94 | 50.62 | 51.25 |
PL-Marker [37] | - | - | 69.90 | - | - | 53.20 |
EINET | 71.26 | 71.43 | 71.34 | 55.34 | 50.73 | 52.93 |
Model | Entity | Relation | ||||
---|---|---|---|---|---|---|
Precision | Recall | F1 | Precision | Recall | F1 | |
BiLSTM + SDP [38] | 82.70 | 86.70 | 84.60 | 67.50 | 75.80 | 71.40 |
Multi-head [34] | 84.72 | 88.16 | 86.40 | 72.10 | 77.24 | 74.58 |
Multi-head+AT [16] | - | - | 86.73 | - | - | 75.52 |
Relation-Metric [32] | 86.16 | 88.08 | 87.11 | 77.36 | 77.25 | 77.29 |
SpERT(without overlap) [26] | 89.26 | 89.26 | 89.25 | 78.09 | 80.43 | 79.24 |
SpERT(with overlap) [26] | 88.99 | 89.59 | 89.28 | 77.77 | 79.96 | 78.84 |
PFN [40] | - | - | 89.60 | - | - | 80.00 |
Two are better than one [36] | - | - | 89.70 | - | - | 80.10 |
EINET(without overlap) | 90.03 | 91.12 | 90.57 | 80.38 | 83.79 | 82.04 |
EINET(with overlap) | 89.69 | 91.29 | 90.48 | 79.74 | 83.11 | 81.38 |
Model | Entity(F1) | Relation(F1) | |||
---|---|---|---|---|---|
Micro-Average | Macro-Average | Micro-Average | Macro-Average | ||
1 | EINET | 91.31 | 88.51 | 74.90 | 75.91 |
2 | w/o SRL | 89.93 | 87.57 | 72.86 | 73.50 |
3 | w/o | 90.39 | 87.50 | 73.73 | 74.62 |
4 | w/o | 90.72 | 88.01 | 73.46 | 74.63 |
5 | w/o and | 90.19 | 87.33 | 73.16 | 74.39 |
6 | w/o global semantics (relation) | 91.01 | 88.10 | 74.26 | 75.78 |
7 | w/o local context length information | 90.85 | 87.87 | 74.07 | 75.52 |
8 | w/o global semantics (relation) and local context length information | 90.40 | 87.40 | 73.56 | 74.71 |
9 | Baseline | 88.94 | 86.25 | 71.47 | 72.87 |
Error Cases | ||
---|---|---|
Incorrect Spans | Sentences | Cutaneous necrosis after injection of polyethylene glycol—modified interferon alfa. |
Ground Truth | Entities: {’type’: ’Adverse-Effect’, Cutaneous necrosis} {’type’: ’Drug’, interferon alfa} Relations: (interferon alfa, ’Adverse-Effect’, Cutaneous necrosis) | |
Our Model | Entities: {’type’: ’Adverse-Effect’, Cutaneous necrosis} {’type’: ’Drug’, interferon} Relations: (interferon, ’Adverse-Effect’, Cutaneous necrosis) | |
Logical | Sentences | “NRC has a broad programmatic concern that the pressure to meet unrealistic schedule milestones may leave DOE insufficient time to plan and to execute proper technical information-gathering activities.” said Robert Bernero, chief of waste disposal for the commission. |
Ground Truth | Entities: {’type’: ’Org’, NRC} {’type’: ’Org’, DOE} {’type’: ’Peop’, Robert Bernero} Relations: (Robert Bernero, Work-For, DOE) | |
Our Model | Entities: {’type’: ’Org’, NRC} {’type’: ’Org’, DOE} {’type’: ’Peop’, Robert Bernero} Relations: | |
Missing Annotation | Sentences | Hatcher also fled to the Onondaga territory but has since moved to a Shoshone-Bannock reservation in Idaho. |
Ground Truth | Entities: {’type’: ’Peop’, Hatcher } {’type’: ’Loc’, Onondaga territory} {’type’: ’Loc’, Shoshone-Bannock} {’type’: ’Loc’, Idaho} Relations: (Shoshone-Bannock, Located-In, Idaho) | |
Our Model | Entities: {’type’: ’Peop’, Hatcher } {’type’: ’Loc’, Onondaga territory} {’type’: ’Loc’, Shoshone-Bannock} {’type’: ’Loc’, Idaho} Relations: (Shoshone-Bannock, Located-In, Idaho) (Hatcher, Live-In, Onondaga territory) (Hatcher, Live-In, Shoshone-Bannock) (Hatcher, Live-In, Idaho) |
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Wu, H.; Huang, J. Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information. Appl. Sci. 2022, 12, 6231. https://doi.org/10.3390/app12126231
Wu H, Huang J. Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information. Applied Sciences. 2022; 12(12):6231. https://doi.org/10.3390/app12126231
Chicago/Turabian StyleWu, Huiyan, and Jun Huang. 2022. "Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information" Applied Sciences 12, no. 12: 6231. https://doi.org/10.3390/app12126231