An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text Article Swipe
Daivik Swarup
,
Ahsaas Bajaj
,
Sheshera Mysore
,
Tim O’Gorman
,
Rajarshi Das
,
Andrew McCallum
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.270
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.270
In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.
Related Topics
Concepts
Computer science
Natural language processing
Artificial intelligence
Parsing
Task (project management)
Metric (unit)
Baseline (sea)
Set (abstract data type)
Sentence
Semantic similarity
Similarity (geometry)
Test set
Information retrieval
Programming language
Oceanography
Management
Operations management
Image (mathematics)
Geology
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2020.findings-emnlp.270
- https://www.aclweb.org/anthology/2020.findings-emnlp.270.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3104921559
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3104921559Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2020.findings-emnlp.270Digital Object Identifier
- Title
-
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural TextWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O’Gorman, Rajarshi Das, Andrew McCallumList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2020.findings-emnlp.270Publisher landing page
- PDF URL
-
https://www.aclweb.org/anthology/2020.findings-emnlp.270.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.aclweb.org/anthology/2020.findings-emnlp.270.pdfDirect OA link when available
- Concepts
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Computer science, Natural language processing, Artificial intelligence, Parsing, Task (project management), Metric (unit), Baseline (sea), Set (abstract data type), Sentence, Semantic similarity, Similarity (geometry), Test set, Information retrieval, Programming language, Oceanography, Management, Operations management, Image (mathematics), Geology, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.absolute | 155, 165 |
| abstract_inverted_index.approach | 74, 143 |
| abstract_inverted_index.baseline | 146 |
| abstract_inverted_index.coverage | 51 |
| abstract_inverted_index.domains, | 2 |
| abstract_inverted_index.explores | 71 |
| abstract_inverted_index.globally | 134 |
| abstract_inverted_index.instance | 124 |
| abstract_inverted_index.network, | 127 |
| abstract_inverted_index.parsing, | 83 |
| abstract_inverted_index.possible | 114 |
| abstract_inverted_index.relation | 77 |
| abstract_inverted_index.semantic | 13, 53, 82, 86 |
| abstract_inverted_index.sentence | 120 |
| abstract_inverted_index.specific | 1, 24 |
| abstract_inverted_index.sub-task | 79 |
| abstract_inverted_index.training | 94 |
| abstract_inverted_index.writing, | 30 |
| abstract_inverted_index.Candidate | 101 |
| abstract_inverted_index.Materials | 168 |
| abstract_inverted_index.different | 34 |
| abstract_inverted_index.employed. | 139 |
| abstract_inverted_index.expensive | 19 |
| abstract_inverted_index.formulaic | 29 |
| abstract_inverted_index.possible, | 132 |
| abstract_inverted_index.relations | 38 |
| abstract_inverted_index.retrieved | 105 |
| abstract_inverted_index.sentences | 47, 91, 103 |
| abstract_inverted_index.Procedural | 170 |
| abstract_inverted_index.especially | 16 |
| abstract_inverted_index.expressing | 37 |
| abstract_inverted_index.parametric | 136 |
| abstract_inverted_index.prediction | 78 |
| abstract_inverted_index.procedural | 5 |
| abstract_inverted_index.scientific | 6 |
| abstract_inverted_index.sentences. | 100 |
| abstract_inverted_index.similarity | 62 |
| abstract_inverted_index.structures | 54 |
| abstract_inverted_index.Experiments | 140 |
| abstract_inverted_index.embeddings. | 108 |
| abstract_inverted_index.outperforms | 144 |
| abstract_inverted_index.Fortunately, | 22 |
| abstract_inverted_index.opportunity, | 68 |
| abstract_inverted_index.structurally | 89 |
| abstract_inverted_index.appropriately | 60 |
| abstract_inverted_index.grammatically | 44 |
| abstract_inverted_index.instance-based | 73 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.70487227 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |