Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning Article Swipe
Guangming Ling
,
Xiaofeng Mu
,
Chao Wang
,
Aiping Xu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi12070296
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi12070296
Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and Generative Pre-trained Transformer (GPT) modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, which mitigates interference from unnecessary variations. We introduce the REB–KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model’s effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi12070296
- https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528
- OA Status
- gold
- Cited By
- 5
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385221022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385221022Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijgi12070296Digital Object Identifier
- Title
-
Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-22Full publication date if available
- Authors
-
Guangming Ling, Xiaofeng Mu, Chao Wang, Aiping XuList of authors in order
- Landing page
-
https://doi.org/10.3390/ijgi12070296Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528Direct OA link when available
- Concepts
-
Computer science, Parsing, Artificial intelligence, Natural language processing, Dependency grammar, Scalability, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385221022 |
|---|---|
| doi | https://doi.org/10.3390/ijgi12070296 |
| ids.doi | https://doi.org/10.3390/ijgi12070296 |
| ids.openalex | https://openalex.org/W4385221022 |
| fwci | 1.27721444 |
| type | article |
| title | Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning |
| awards[0].id | https://openalex.org/G5017888718 |
| awards[0].funder_id | https://openalex.org/F4320335777 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2018YFB2100603 |
| awards[0].funder_display_name | National Key Research and Development Program of China |
| awards[1].id | https://openalex.org/G8935175774 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 41890822 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 7 |
| biblio.volume | 12 |
| biblio.last_page | 296 |
| biblio.first_page | 296 |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Topic Modeling |
| topics[1].id | https://openalex.org/T10181 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9965000152587891 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Natural Language Processing Techniques |
| topics[2].id | https://openalex.org/T11714 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9700999855995178 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Multimodal Machine Learning Applications |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320335777 |
| funders[1].ror | |
| funders[1].display_name | National Key Research and Development Program of China |
| is_xpac | False |
| apc_list.value | 1400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1515 |
| apc_paid.value | 1400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1515 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.865085244178772 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C186644900 |
| concepts[1].level | 2 |
| concepts[1].score | 0.696424663066864 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q194152 |
| concepts[1].display_name | Parsing |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6783225536346436 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C204321447 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6377702951431274 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[3].display_name | Natural language processing |
| concepts[4].id | https://openalex.org/C164883195 |
| concepts[4].level | 3 |
| concepts[4].score | 0.49805498123168945 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q674834 |
| concepts[4].display_name | Dependency grammar |
| concepts[5].id | https://openalex.org/C48044578 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4132829010486603 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[5].display_name | Scalability |
| concepts[6].id | https://openalex.org/C77088390 |
| concepts[6].level | 1 |
| concepts[6].score | 0.11085096001625061 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[6].display_name | Database |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.865085244178772 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/parsing |
| keywords[1].score | 0.696424663066864 |
| keywords[1].display_name | Parsing |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6783225536346436 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/natural-language-processing |
| keywords[3].score | 0.6377702951431274 |
| keywords[3].display_name | Natural language processing |
| keywords[4].id | https://openalex.org/keywords/dependency-grammar |
| keywords[4].score | 0.49805498123168945 |
| keywords[4].display_name | Dependency grammar |
| keywords[5].id | https://openalex.org/keywords/scalability |
| keywords[5].score | 0.4132829010486603 |
| keywords[5].display_name | Scalability |
| keywords[6].id | https://openalex.org/keywords/database |
| keywords[6].score | 0.11085096001625061 |
| keywords[6].display_name | Database |
| language | en |
| locations[0].id | doi:10.3390/ijgi12070296 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764431341 |
| locations[0].source.issn | 2220-9964 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2220-9964 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | ISPRS International Journal of Geo-Information |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | ISPRS International Journal of Geo-Information |
| locations[0].landing_page_url | https://doi.org/10.3390/ijgi12070296 |
| locations[1].id | pmh:oai:doaj.org/article:ecc2f1fbeb2c43ccb37740f707c3b672 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | ISPRS International Journal of Geo-Information, Vol 12, Iss 7, p 296 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/ecc2f1fbeb2c43ccb37740f707c3b672 |
| locations[2].id | pmh:oai:mdpi.com:/2220-9964/12/7/296/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | ISPRS International Journal of Geo-Information; Volume 12; Issue 7; Pages: 296 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/ijgi12070296 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5065190048 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8263-4215 |
| authorships[0].author.display_name | Guangming Ling |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I37461747 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science, Wuhan University, Wuhan 430072, China |
| authorships[0].institutions[0].id | https://openalex.org/I37461747 |
| authorships[0].institutions[0].ror | https://ror.org/033vjfk17 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I37461747 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Wuhan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Guangming Ling |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Computer Science, Wuhan University, Wuhan 430072, China |
| authorships[1].author.id | https://openalex.org/A5101597421 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0780-0812 |
| authorships[1].author.display_name | Xiaofeng Mu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210142622, https://openalex.org/I47720641 |
| authorships[1].affiliations[0].raw_affiliation_string | Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430074, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I37461747 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Computer Science, Wuhan University, Wuhan 430072, China |
| authorships[1].institutions[0].id | https://openalex.org/I47720641 |
| authorships[1].institutions[0].ror | https://ror.org/00p991c53 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I47720641 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Huazhong University of Science and Technology |
| authorships[1].institutions[1].id | https://openalex.org/I4210142622 |
| authorships[1].institutions[1].ror | https://ror.org/047c53f83 |
| authorships[1].institutions[1].type | healthcare |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210142622 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Wuhan Children's Hospital |
| authorships[1].institutions[2].id | https://openalex.org/I37461747 |
| authorships[1].institutions[2].ror | https://ror.org/033vjfk17 |
| authorships[1].institutions[2].type | education |
| authorships[1].institutions[2].lineage | https://openalex.org/I37461747 |
| authorships[1].institutions[2].country_code | CN |
| authorships[1].institutions[2].display_name | Wuhan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xiaofeng Mu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computer Science, Wuhan University, Wuhan 430072, China, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430074, China |
| authorships[2].author.id | https://openalex.org/A5100406991 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4737-0717 |
| authorships[2].author.display_name | Chao Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I37461747, https://openalex.org/I4210118728 |
| authorships[2].affiliations[0].raw_affiliation_string | The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210118728 |
| authorships[2].institutions[0].ror | https://ror.org/02bpap860 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210118728 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing |
| authorships[2].institutions[1].id | https://openalex.org/I37461747 |
| authorships[2].institutions[1].ror | https://ror.org/033vjfk17 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I37461747 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Wuhan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Chao Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China |
| authorships[3].author.id | https://openalex.org/A5102770758 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9854-852X |
| authorships[3].author.display_name | Aiping Xu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I37461747 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer Science, Wuhan University, Wuhan 430072, China |
| authorships[3].institutions[0].id | https://openalex.org/I37461747 |
| authorships[3].institutions[0].ror | https://ror.org/033vjfk17 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I37461747 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Wuhan University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Aiping Xu |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | School of Computer Science, Wuhan University, Wuhan 430072, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-07-25T00:00:00 |
| display_name | Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Topic Modeling |
| related_works | https://openalex.org/W2251084681, https://openalex.org/W287510790, https://openalex.org/W2098784136, https://openalex.org/W2968543375, https://openalex.org/W2571817549, https://openalex.org/W1970099335, https://openalex.org/W1541975828, https://openalex.org/W2888625260, https://openalex.org/W2159336305, https://openalex.org/W3035970863 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/ijgi12070296 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764431341 |
| best_oa_location.source.issn | 2220-9964 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2220-9964 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | ISPRS International Journal of Geo-Information |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | ISPRS International Journal of Geo-Information |
| best_oa_location.landing_page_url | https://doi.org/10.3390/ijgi12070296 |
| primary_location.id | doi:10.3390/ijgi12070296 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764431341 |
| primary_location.source.issn | 2220-9964 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2220-9964 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | ISPRS International Journal of Geo-Information |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2220-9964/12/7/296/pdf?version=1690185528 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | ISPRS International Journal of Geo-Information |
| primary_location.landing_page_url | https://doi.org/10.3390/ijgi12070296 |
| publication_date | 2023-07-22 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3024097351, https://openalex.org/W4310154945, https://openalex.org/W2790313525, https://openalex.org/W4306680578, https://openalex.org/W2909828422, https://openalex.org/W4327499192, https://openalex.org/W2973619314, https://openalex.org/W3168656614, https://openalex.org/W4385571076, https://openalex.org/W4385567149, https://openalex.org/W6778883912, https://openalex.org/W3173777717, https://openalex.org/W4221148939, https://openalex.org/W4385766704, https://openalex.org/W3122241445, https://openalex.org/W3185341429, https://openalex.org/W6810313920, https://openalex.org/W3156470785, https://openalex.org/W3172943453, https://openalex.org/W4285089646, https://openalex.org/W3114319529, https://openalex.org/W3175552668, https://openalex.org/W2963642108, https://openalex.org/W6809646742, https://openalex.org/W2970641574, https://openalex.org/W3100806282, https://openalex.org/W2970476646, https://openalex.org/W3172642864, https://openalex.org/W4212903126, https://openalex.org/W3176023514, https://openalex.org/W4221143046 |
| referenced_works_count | 31 |
| abstract_inverted_index.a | 3, 59, 74, 104 |
| abstract_inverted_index.To | 47 |
| abstract_inverted_index.We | 98, 129 |
| abstract_inverted_index.by | 94 |
| abstract_inverted_index.in | 6, 39, 181, 206 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 20, 37, 114, 179 |
| abstract_inverted_index.on | 66, 186 |
| abstract_inverted_index.to | 26, 53, 90, 117, 160 |
| abstract_inverted_index.we | 56 |
| abstract_inverted_index.GPT | 167 |
| abstract_inverted_index.Our | 192 |
| abstract_inverted_index.The | 14, 150 |
| abstract_inverted_index.and | 17, 51, 84, 111, 121, 145, 155, 163, 171, 189, 196, 210 |
| abstract_inverted_index.are | 158, 200 |
| abstract_inverted_index.due | 25 |
| abstract_inverted_index.for | 11, 138, 169 |
| abstract_inverted_index.its | 203 |
| abstract_inverted_index.raw | 153 |
| abstract_inverted_index.the | 44, 67, 100, 131, 166 |
| abstract_inverted_index.BERT | 80, 146 |
| abstract_inverted_index.data | 188 |
| abstract_inverted_index.form | 161 |
| abstract_inverted_index.from | 126 |
| abstract_inverted_index.into | 165 |
| abstract_inverted_index.task | 5, 32 |
| abstract_inverted_index.text | 157 |
| abstract_inverted_index.(GPT) | 88 |
| abstract_inverted_index.(ICL) | 70 |
| abstract_inverted_index.adapt | 52 |
| abstract_inverted_index.based | 65 |
| abstract_inverted_index.broad | 197 |
| abstract_inverted_index.model | 64, 118, 168 |
| abstract_inverted_index.novel | 60 |
| abstract_inverted_index.small | 105 |
| abstract_inverted_index.text, | 154 |
| abstract_inverted_index.their | 27 |
| abstract_inverted_index.these | 49 |
| abstract_inverted_index.types | 116 |
| abstract_inverted_index.using | 103, 141 |
| abstract_inverted_index.which | 123, 134 |
| abstract_inverted_index.CapICL | 72, 180 |
| abstract_inverted_index.impact | 205 |
| abstract_inverted_index.levels | 36 |
| abstract_inverted_index.Address | 0 |
| abstract_inverted_index.CapICL, | 58 |
| abstract_inverted_index.Chinese | 12, 21, 61 |
| abstract_inverted_index.address | 40, 48, 62, 115, 119, 172 |
| abstract_inverted_index.complex | 15 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.enhance | 91 |
| abstract_inverted_index.further | 42 |
| abstract_inverted_index.natural | 7, 207 |
| abstract_inverted_index.parsing | 1, 45, 63, 92, 140 |
| abstract_inverted_index.present | 23 |
| abstract_inverted_index.prompts | 162 |
| abstract_inverted_index.propose | 57 |
| abstract_inverted_index.regular | 77, 142 |
| abstract_inverted_index.require | 34 |
| abstract_inverted_index.results | 175 |
| abstract_inverted_index.samples | 137 |
| abstract_inverted_index.selects | 135 |
| abstract_inverted_index.similar | 136 |
| abstract_inverted_index.varying | 35 |
| abstract_inverted_index.Learning | 69 |
| abstract_inverted_index.accuracy | 93 |
| abstract_inverted_index.boundary | 112 |
| abstract_inverted_index.combined | 159 |
| abstract_inverted_index.dataset, | 107 |
| abstract_inverted_index.features | 19, 113 |
| abstract_inverted_index.inherent | 28 |
| abstract_inverted_index.inputted | 164 |
| abstract_inverted_index.language | 8, 208 |
| abstract_inverted_index.matching | 144 |
| abstract_inverted_index.modeling | 89 |
| abstract_inverted_index.parsing. | 173 |
| abstract_inverted_index.patterns | 110 |
| abstract_inverted_index.positive | 204 |
| abstract_inverted_index.process. | 46 |
| abstract_inverted_index.reducing | 184 |
| abstract_inverted_index.samples, | 152 |
| abstract_inverted_index.selected | 151 |
| abstract_inverted_index.semantic | 18, 81, 147 |
| abstract_inverted_index.sequence | 75, 101 |
| abstract_inverted_index.systems. | 213 |
| abstract_inverted_index.ICL-based | 139 |
| abstract_inverted_index.REB–KNN | 132 |
| abstract_inverted_index.addresses | 22 |
| abstract_inverted_index.annotated | 106, 187 |
| abstract_inverted_index.capturing | 108 |
| abstract_inverted_index.construct | 99 |
| abstract_inverted_index.different | 31 |
| abstract_inverted_index.generator | 102 |
| abstract_inverted_index.introduce | 130 |
| abstract_inverted_index.leverages | 73 |
| abstract_inverted_index.matching, | 79 |
| abstract_inverted_index.mitigates | 124 |
| abstract_inverted_index.model’s | 193 |
| abstract_inverted_index.potential | 199 |
| abstract_inverted_index.scenarios | 33 |
| abstract_inverted_index.structure | 16, 120 |
| abstract_inverted_index.Generative | 85 |
| abstract_inverted_index.In-Context | 68 |
| abstract_inverted_index.addresses. | 13 |
| abstract_inverted_index.algorithm, | 133 |
| abstract_inverted_index.ambiguity. | 29 |
| abstract_inverted_index.challenges | 24, 50 |
| abstract_inverted_index.contextual | 96 |
| abstract_inverted_index.dependency | 185 |
| abstract_inverted_index.expression | 78, 143 |
| abstract_inverted_index.framework. | 71 |
| abstract_inverted_index.generator, | 76 |
| abstract_inverted_index.prediction | 170 |
| abstract_inverted_index.processing | 209 |
| abstract_inverted_index.resources. | 191 |
| abstract_inverted_index.semantics, | 122 |
| abstract_inverted_index.showcasing | 202 |
| abstract_inverted_index.similarity | 82, 148 |
| abstract_inverted_index.validated, | 201 |
| abstract_inverted_index.Pre-trained | 86 |
| abstract_inverted_index.Transformer | 87 |
| abstract_inverted_index.application | 198 |
| abstract_inverted_index.components, | 41 |
| abstract_inverted_index.demonstrate | 176 |
| abstract_inverted_index.explanatory | 156 |
| abstract_inverted_index.granularity | 38 |
| abstract_inverted_index.information | 212 |
| abstract_inverted_index.processing, | 9 |
| abstract_inverted_index.significant | 177 |
| abstract_inverted_index.unnecessary | 127 |
| abstract_inverted_index.variations. | 128 |
| abstract_inverted_index.Experimental | 174 |
| abstract_inverted_index.achievements | 178 |
| abstract_inverted_index.complicating | 43 |
| abstract_inverted_index.computation, | 83 |
| abstract_inverted_index.computation. | 149 |
| abstract_inverted_index.distribution | 109 |
| abstract_inverted_index.geographical | 211 |
| abstract_inverted_index.information. | 97 |
| abstract_inverted_index.interference | 125 |
| abstract_inverted_index.low-resource | 54, 182 |
| abstract_inverted_index.particularly | 10 |
| abstract_inverted_index.Additionally, | 30 |
| abstract_inverted_index.adaptability, | 195 |
| abstract_inverted_index.computational | 190 |
| abstract_inverted_index.environments, | 55, 183 |
| abstract_inverted_index.incorporating | 95 |
| abstract_inverted_index.effectiveness, | 194 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5102770758 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I37461747 |
| citation_normalized_percentile.value | 0.80745488 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |