Fast Extraction of Word Embedding from Q-contexts Article Swipe
Junsheng Kong
,
Weizhao Li
,
Zeyi Liu
,
Ben Liao
,
Jiezhong Qiu
,
Chang‐Yu Hsieh
,
Yi Cai
,
Shengyu Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3459637.3482343
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3459637.3482343
The notion of word embedding plays a fundamental role in natural language\nprocessing (NLP). However, pre-training word embedding for very large-scale\nvocabulary is computationally challenging for most existing methods. In this\nwork, we show that with merely a small fraction of contexts (Q-contexts)which\nare typical in the whole corpus (and their mutual information with words), one\ncan construct high-quality word embedding with negligible errors. Mutual\ninformation between contexts and words can be encoded canonically as a sampling\nstate, thus, Q-contexts can be fast constructed. Furthermore, we present an\nefficient and effective WEQ method, which is capable of extracting word\nembedding directly from these typical contexts. In practical scenarios, our\nalgorithm runs 11$\\sim$13 times faster than well-established methods. By\ncomparing with well-known methods such as matrix factorization, word2vec,\nGloVeand fasttext, we demonstrate that our method achieves comparable\nperformance on a variety of downstream NLP tasks, and in the meanwhile\nmaintains run-time and resource advantages over all these baselines.\n
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3459637.3482343
- OA Status
- green
- Cited By
- 2
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3199747066
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3199747066Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3459637.3482343Digital Object Identifier
- Title
-
Fast Extraction of Word Embedding from Q-contextsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-26Full publication date if available
- Authors
-
Junsheng Kong, Weizhao Li, Zeyi Liu, Ben Liao, Jiezhong Qiu, Chang‐Yu Hsieh, Yi Cai, Shengyu ZhangList of authors in order
- Landing page
-
https://doi.org/10.1145/3459637.3482343Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.07084Direct OA link when available
- Concepts
-
Computer science, Word2vec, Word embedding, Word (group theory), Embedding, Natural language processing, Artificial intelligence, Vocabulary, Variety (cybernetics), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3199747066 |
|---|---|
| doi | https://doi.org/10.1145/3459637.3482343 |
| ids.doi | https://doi.org/10.1145/3459637.3482343 |
| ids.mag | 3199747066 |
| ids.openalex | https://openalex.org/W3199747066 |
| fwci | 0.28220715 |
| type | preprint |
| title | Fast Extraction of Word Embedding from Q-contexts |
| awards[0].id | https://openalex.org/G491278422 |
| awards[0].funder_id | https://openalex.org/F4320335787 |
| awards[0].display_name | |
| awards[0].funder_award_id | D2210010,D2200150,D2201300 |
| awards[0].funder_display_name | Fundamental Research Funds for the Central Universities |
| awards[1].id | https://openalex.org/G8189774080 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 62076100 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| awards[2].id | https://openalex.org/G8718620462 |
| awards[2].funder_id | https://openalex.org/F4320335795 |
| awards[2].display_name | |
| awards[2].funder_award_id | 2020B0101100002 |
| awards[2].funder_display_name | Science and Technology Planning Project of Guangdong Province |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 882 |
| biblio.first_page | 873 |
| 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 | 1.0 |
| 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 | 0.9998999834060669 |
| 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/T11550 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9984999895095825 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Text and Document Classification Technologies |
| 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/F4320335787 |
| funders[1].ror | |
| funders[1].display_name | Fundamental Research Funds for the Central Universities |
| funders[2].id | https://openalex.org/F4320335795 |
| funders[2].ror | |
| funders[2].display_name | Science and Technology Planning Project of Guangdong Province |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8006882071495056 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2776461190 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7734689116477966 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q22673982 |
| concepts[1].display_name | Word2vec |
| concepts[2].id | https://openalex.org/C2777462759 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6546187400817871 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q18395344 |
| concepts[2].display_name | Word embedding |
| concepts[3].id | https://openalex.org/C90805587 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6482841372489929 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q10944557 |
| concepts[3].display_name | Word (group theory) |
| concepts[4].id | https://openalex.org/C41608201 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6406123638153076 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q980509 |
| concepts[4].display_name | Embedding |
| concepts[5].id | https://openalex.org/C204321447 |
| concepts[5].level | 1 |
| concepts[5].score | 0.6236016750335693 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[5].display_name | Natural language processing |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5959158539772034 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C2777601683 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5765365958213806 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q6499736 |
| concepts[7].display_name | Vocabulary |
| concepts[8].id | https://openalex.org/C136197465 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4565044343471527 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1729295 |
| concepts[8].display_name | Variety (cybernetics) |
| concepts[9].id | https://openalex.org/C41895202 |
| concepts[9].level | 1 |
| concepts[9].score | 0.13337770104408264 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[9].display_name | Linguistics |
| concepts[10].id | https://openalex.org/C138885662 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[10].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8006882071495056 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/word2vec |
| keywords[1].score | 0.7734689116477966 |
| keywords[1].display_name | Word2vec |
| keywords[2].id | https://openalex.org/keywords/word-embedding |
| keywords[2].score | 0.6546187400817871 |
| keywords[2].display_name | Word embedding |
| keywords[3].id | https://openalex.org/keywords/word |
| keywords[3].score | 0.6482841372489929 |
| keywords[3].display_name | Word (group theory) |
| keywords[4].id | https://openalex.org/keywords/embedding |
| keywords[4].score | 0.6406123638153076 |
| keywords[4].display_name | Embedding |
| keywords[5].id | https://openalex.org/keywords/natural-language-processing |
| keywords[5].score | 0.6236016750335693 |
| keywords[5].display_name | Natural language processing |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.5959158539772034 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/vocabulary |
| keywords[7].score | 0.5765365958213806 |
| keywords[7].display_name | Vocabulary |
| keywords[8].id | https://openalex.org/keywords/variety |
| keywords[8].score | 0.4565044343471527 |
| keywords[8].display_name | Variety (cybernetics) |
| keywords[9].id | https://openalex.org/keywords/linguistics |
| keywords[9].score | 0.13337770104408264 |
| keywords[9].display_name | Linguistics |
| language | en |
| locations[0].id | doi:10.1145/3459637.3482343 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the 30th ACM International Conference on Information & Knowledge Management |
| locations[0].landing_page_url | https://doi.org/10.1145/3459637.3482343 |
| locations[1].id | pmh:oai:arXiv.org:2109.07084 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/2109.07084 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2109.07084 |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5045187263 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Junsheng Kong |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I90610280 |
| authorships[0].affiliations[0].raw_affiliation_string | South China University of Technology, Guangzhou, China |
| authorships[0].institutions[0].id | https://openalex.org/I90610280 |
| authorships[0].institutions[0].ror | https://ror.org/0530pts50 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I90610280 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | South China University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Junsheng Kong |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | South China University of Technology, Guangzhou, China |
| authorships[1].author.id | https://openalex.org/A5006326179 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4394-4987 |
| authorships[1].author.display_name | Weizhao Li |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I90610280 |
| authorships[1].affiliations[0].raw_affiliation_string | South China University of Technology, Guangzhou, China |
| authorships[1].institutions[0].id | https://openalex.org/I90610280 |
| authorships[1].institutions[0].ror | https://ror.org/0530pts50 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I90610280 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | South China University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Weizhao Li |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | South China University of Technology, Guangzhou, China |
| authorships[2].author.id | https://openalex.org/A5031172524 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Zeyi Liu |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I241749 |
| authorships[2].affiliations[0].raw_affiliation_string | University of Cambridge, Cambridge, United Kingdom |
| authorships[2].institutions[0].id | https://openalex.org/I241749 |
| authorships[2].institutions[0].ror | https://ror.org/013meh722 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I241749 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University of Cambridge |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zeyi Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University of Cambridge, Cambridge, United Kingdom |
| authorships[3].author.id | https://openalex.org/A5064163623 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5084-2682 |
| authorships[3].author.display_name | Ben Liao |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I2250653659 |
| authorships[3].affiliations[0].raw_affiliation_string | Tencent Quantum Lab, Shenzhen, China |
| authorships[3].institutions[0].id | https://openalex.org/I2250653659 |
| authorships[3].institutions[0].ror | https://ror.org/00hhjss72 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I2250653659 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Tencent (China) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ben Liao |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Tencent Quantum Lab, Shenzhen, China |
| authorships[4].author.id | https://openalex.org/A5083333630 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-9514-0708 |
| authorships[4].author.display_name | Jiezhong Qiu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I99065089 |
| authorships[4].affiliations[0].raw_affiliation_string | Tsinghua University, Beijing, China |
| authorships[4].institutions[0].id | https://openalex.org/I99065089 |
| authorships[4].institutions[0].ror | https://ror.org/03cve4549 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I99065089 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Tsinghua University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Jiezhong Qiu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Tsinghua University, Beijing, China |
| authorships[5].author.id | https://openalex.org/A5008663435 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-6242-4218 |
| authorships[5].author.display_name | Chang‐Yu Hsieh |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I2250653659 |
| authorships[5].affiliations[0].raw_affiliation_string | Tencent Quantum Lab, Shenzhen, China |
| authorships[5].institutions[0].id | https://openalex.org/I2250653659 |
| authorships[5].institutions[0].ror | https://ror.org/00hhjss72 |
| authorships[5].institutions[0].type | company |
| authorships[5].institutions[0].lineage | https://openalex.org/I2250653659 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Tencent (China) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Chang-Yu Hsieh |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Tencent Quantum Lab, Shenzhen, China |
| authorships[6].author.id | https://openalex.org/A5089123257 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1767-789X |
| authorships[6].author.display_name | Yi Cai |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I90610280 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Software Engineering, South China University of Technology, Guangzhou, China |
| authorships[6].institutions[0].id | https://openalex.org/I90610280 |
| authorships[6].institutions[0].ror | https://ror.org/0530pts50 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I90610280 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | South China University of Technology |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Yi Cai |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Software Engineering, South China University of Technology, Guangzhou, China |
| authorships[7].author.id | https://openalex.org/A5100757082 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-5907-2277 |
| authorships[7].author.display_name | Shengyu Zhang |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I2250653659 |
| authorships[7].affiliations[0].raw_affiliation_string | Tencent Quantum Lab, Shenzhen, China |
| authorships[7].institutions[0].id | https://openalex.org/I2250653659 |
| authorships[7].institutions[0].ror | https://ror.org/00hhjss72 |
| authorships[7].institutions[0].type | company |
| authorships[7].institutions[0].lineage | https://openalex.org/I2250653659 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Tencent (China) |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Shengyu Zhang |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Tencent Quantum Lab, Shenzhen, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2109.07084 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Fast Extraction of Word Embedding from Q-contexts |
| has_fulltext | False |
| 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 | 1.0 |
| 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/W2946409105, https://openalex.org/W2905749112, https://openalex.org/W3152932816, https://openalex.org/W2985392712, https://openalex.org/W4388996947, https://openalex.org/W3133567596, https://openalex.org/W2798009317, https://openalex.org/W3203949288, https://openalex.org/W3099354896, https://openalex.org/W4287599800 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2109.07084 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2109.07084 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2109.07084 |
| primary_location.id | doi:10.1145/3459637.3482343 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the 30th ACM International Conference on Information & Knowledge Management |
| primary_location.landing_page_url | https://doi.org/10.1145/3459637.3482343 |
| publication_date | 2021-10-26 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2518186251, https://openalex.org/W2493916176, https://openalex.org/W2159637323, https://openalex.org/W2095958485, https://openalex.org/W2808726247, https://openalex.org/W2147152072, https://openalex.org/W1998269045, https://openalex.org/W1979750072, https://openalex.org/W2983766176, https://openalex.org/W2964289193, https://openalex.org/W3034617741, https://openalex.org/W2250539671, https://openalex.org/W2962739339, https://openalex.org/W3034457371, https://openalex.org/W2880299821, https://openalex.org/W2144578941, https://openalex.org/W2964089577, https://openalex.org/W2997185401, https://openalex.org/W2986753851, https://openalex.org/W2824731080, https://openalex.org/W3111333709, https://openalex.org/W1662133657, https://openalex.org/W1938444229, https://openalex.org/W2125031621, https://openalex.org/W2896457183, https://openalex.org/W3101860695, https://openalex.org/W2949541494, https://openalex.org/W1614298861, https://openalex.org/W2153579005, https://openalex.org/W3101135395, https://openalex.org/W2950577311, https://openalex.org/W2963341956, https://openalex.org/W2945077829, https://openalex.org/W2948223045, https://openalex.org/W2103956991, https://openalex.org/W2251939518, https://openalex.org/W2579198251, https://openalex.org/W2491030916, https://openalex.org/W2171671424, https://openalex.org/W2251291469, https://openalex.org/W2112184938, https://openalex.org/W2097732278, https://openalex.org/W2553316781, https://openalex.org/W2887428522, https://openalex.org/W2170682101, https://openalex.org/W2950133940, https://openalex.org/W1832693441 |
| referenced_works_count | 47 |
| abstract_inverted_index.a | 6, 34, 69, 125 |
| abstract_inverted_index.In | 27, 96 |
| abstract_inverted_index.as | 68, 112 |
| abstract_inverted_index.be | 65, 74 |
| abstract_inverted_index.in | 9, 41, 132 |
| abstract_inverted_index.is | 20, 86 |
| abstract_inverted_index.of | 2, 37, 88, 127 |
| abstract_inverted_index.on | 124 |
| abstract_inverted_index.we | 29, 78, 117 |
| abstract_inverted_index.NLP | 129 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.WEQ | 83 |
| abstract_inverted_index.all | 140 |
| abstract_inverted_index.and | 62, 81, 131, 136 |
| abstract_inverted_index.can | 64, 73 |
| abstract_inverted_index.for | 17, 23 |
| abstract_inverted_index.our | 120 |
| abstract_inverted_index.the | 42, 133 |
| abstract_inverted_index.(and | 45 |
| abstract_inverted_index.fast | 75 |
| abstract_inverted_index.from | 92 |
| abstract_inverted_index.most | 24 |
| abstract_inverted_index.over | 139 |
| abstract_inverted_index.role | 8 |
| abstract_inverted_index.runs | 100 |
| abstract_inverted_index.show | 30 |
| abstract_inverted_index.such | 111 |
| abstract_inverted_index.than | 104 |
| abstract_inverted_index.that | 31, 119 |
| abstract_inverted_index.very | 18 |
| abstract_inverted_index.with | 32, 49, 56, 108 |
| abstract_inverted_index.word | 3, 15, 54 |
| abstract_inverted_index.plays | 5 |
| abstract_inverted_index.small | 35 |
| abstract_inverted_index.their | 46 |
| abstract_inverted_index.these | 93, 141 |
| abstract_inverted_index.thus, | 71 |
| abstract_inverted_index.times | 102 |
| abstract_inverted_index.which | 85 |
| abstract_inverted_index.whole | 43 |
| abstract_inverted_index.words | 63 |
| abstract_inverted_index.(NLP). | 12 |
| abstract_inverted_index.corpus | 44 |
| abstract_inverted_index.faster | 103 |
| abstract_inverted_index.matrix | 113 |
| abstract_inverted_index.merely | 33 |
| abstract_inverted_index.method | 121 |
| abstract_inverted_index.mutual | 47 |
| abstract_inverted_index.notion | 1 |
| abstract_inverted_index.tasks, | 130 |
| abstract_inverted_index.between | 60 |
| abstract_inverted_index.capable | 87 |
| abstract_inverted_index.encoded | 66 |
| abstract_inverted_index.errors. | 58 |
| abstract_inverted_index.method, | 84 |
| abstract_inverted_index.methods | 110 |
| abstract_inverted_index.natural | 10 |
| abstract_inverted_index.present | 79 |
| abstract_inverted_index.typical | 40, 94 |
| abstract_inverted_index.variety | 126 |
| abstract_inverted_index.words), | 50 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.achieves | 122 |
| abstract_inverted_index.contexts | 38, 61 |
| abstract_inverted_index.directly | 91 |
| abstract_inverted_index.existing | 25 |
| abstract_inverted_index.fraction | 36 |
| abstract_inverted_index.methods. | 26, 106 |
| abstract_inverted_index.one\ncan | 51 |
| abstract_inverted_index.resource | 137 |
| abstract_inverted_index.run-time | 135 |
| abstract_inverted_index.construct | 52 |
| abstract_inverted_index.contexts. | 95 |
| abstract_inverted_index.effective | 82 |
| abstract_inverted_index.embedding | 4, 16, 55 |
| abstract_inverted_index.fasttext, | 116 |
| abstract_inverted_index.practical | 97 |
| abstract_inverted_index.Q-contexts | 72 |
| abstract_inverted_index.advantages | 138 |
| abstract_inverted_index.downstream | 128 |
| abstract_inverted_index.extracting | 89 |
| abstract_inverted_index.negligible | 57 |
| abstract_inverted_index.scenarios, | 98 |
| abstract_inverted_index.well-known | 109 |
| abstract_inverted_index.11$\\sim$13 | 101 |
| abstract_inverted_index.canonically | 67 |
| abstract_inverted_index.challenging | 22 |
| abstract_inverted_index.demonstrate | 118 |
| abstract_inverted_index.fundamental | 7 |
| abstract_inverted_index.information | 48 |
| abstract_inverted_index.this\nwork, | 28 |
| abstract_inverted_index.Furthermore, | 77 |
| abstract_inverted_index.baselines.\n | 142 |
| abstract_inverted_index.constructed. | 76 |
| abstract_inverted_index.high-quality | 53 |
| abstract_inverted_index.pre-training | 14 |
| abstract_inverted_index.By\ncomparing | 107 |
| abstract_inverted_index.an\nefficient | 80 |
| abstract_inverted_index.factorization, | 114 |
| abstract_inverted_index.our\nalgorithm | 99 |
| abstract_inverted_index.computationally | 21 |
| abstract_inverted_index.word\nembedding | 90 |
| abstract_inverted_index.sampling\nstate, | 70 |
| abstract_inverted_index.well-established | 105 |
| abstract_inverted_index.Mutual\ninformation | 59 |
| abstract_inverted_index.word2vec,\nGloVeand | 115 |
| abstract_inverted_index.language\nprocessing | 11 |
| abstract_inverted_index.meanwhile\nmaintains | 134 |
| abstract_inverted_index.(Q-contexts)which\nare | 39 |
| abstract_inverted_index.comparable\nperformance | 123 |
| abstract_inverted_index.large-scale\nvocabulary | 19 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.64277913 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |