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Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Luan, Yi, He, Luheng, Ostendorf, Mari, Hajishirzi, Hannaneh·2018·Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/d18-1360
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CC-BY · From the PDF
Figure 2 Overview of the multitask setup, where all three tasks are treated as classification problems on top of shared span representations. Dotted arcs indicate the normalization space for each task.
Figure 2 Overview of the multitask setup, where all three tasks are treated as classification problems on top of shared span representations. Dotted arcs indicate the normalization space for each task.CC-BY · From the PDF
Figure 3 Knowledge graph construction process.
Figure 3 Knowledge graph construction process.CC-BY · From the PDF
Figure 4 A part of an automatically constructed scientific knowledge graph with the most frequent neighbors of the scientific term statistical machine translation (SMT) on the graph. For simplicity we denote Used-for (Reverse) as Uses , Evaluated-for (Reverse) as Evaluated-by , and replace common terms with their acronyms. The original graph and more examples are given Figure 10 in Appendix B.
Figure 4 A part of an automatically constructed scientific knowledge graph with the most frequent neighbors of the scientific term statistical machine translation (SMT) on the graph. For simplicity we denote Used-for (Reverse) as Uses , Evaluated-for (Reverse) as Evaluated-by , and replace common terms with their acronyms. The original graph and more examples are given Figure 10 in Appendix B.CC-BY · From the PDF
Figure 5 Frequency of detected entities with and without coreferece resolution: using coreference reduces the frequency of the generic phrase detection while significantly increasing the frequency of specific phrases. Linking entities through coreference helps disambiguate phrases when generating the knowledge graph.
Figure 5 Frequency of detected entities with and without coreferece resolution: using coreference reduces the frequency of the generic phrase detection while significantly increasing the frequency of specific phrases. Linking entities through coreference helps disambiguate phrases when generating the knowledge graph.CC-BY · From the PDF
Figure 6 Frequency of relation types between pairs of entities: ( left ) automatic speech recognition (ASR) and machine translation (MT), ( right ) conditional random field (CRF) and graphical model (GM). We use the most frequent relation between pairs of entities in the knowledge graph.
Figure 6 Frequency of relation types between pairs of entities: ( left ) automatic speech recognition (ASR) and machine translation (MT), ( right ) conditional random field (CRF) and graphical model (GM). We use the most frequent relation between pairs of entities in the knowledge graph.CC-BY · From the PDF
Figure 7 Historical trend for top applications of the keyphrase neural network in NLP, speech, and CV conference papers we collected. y-axis indicates the ratio of papers that use neural network in the task to the number of papers that is about the task.
Figure 7 Historical trend for top applications of the keyphrase neural network in NLP, speech, and CV conference papers we collected. y-axis indicates the ratio of papers that use neural network in the task to the number of papers that is about the task.CC-BY · From the PDF
Figure 8 Precision/pseudo-recall curves for human evaluation by varying cut-off thresholds. The AUC is 0.751 with coreference, and 0.695 without.
Figure 8 Precision/pseudo-recall curves for human evaluation by varying cut-off thresholds. The AUC is 0.751 with coreference, and 0.695 without.CC-BY · From the PDF
Figure 9 Annotation example 1 from ACL
Figure 9 Annotation example 1 from ACLCC-BY · From the PDF
Figure 10 An example of our automatically generated knowledge graph centered on statistical machine translation . This is the original figure of Figure 4.
Figure 10 An example of our automatically generated knowledge graph centered on statistical machine translation . This is the original figure of Figure 4.CC-BY · From the PDF

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Metadata

Authors
Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H.
Venue
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2018
Pages
3219-3232
DOI
10.18653/v1/d18-1360
Cited by
422
References
3

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