Hierarchical Contextualized Representation for Named Entity Recognition Article Swipe
Ying Luo
,
Fengshun Xiao
,
Hai Zhao
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.02257
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.02257
Named entity recognition (NER) models are typically based on the architecture of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the modeling of single input prevent the full utilization of global information from larger scope, not only in the entire sentence, but also in the entire document (dataset). In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation. In sentence-level, we take different contributions of words in a single sentence into consideration to enhance the sentence representation learned from an independent BiLSTM via label embedding attention mechanism. In document-level, the key-value memory network is adopted to record the document-aware information for each unique word which is sensitive to similarity of context information. Our two-level hierarchical contextualized representations are fused with each input token embedding and corresponding hidden state of BiLSTM, respectively. The experimental results on three benchmark NER datasets (CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset) show that we establish new state-of-the-art results.
Related Topics
Concepts
Computer science
Sentence
Natural language processing
Representation (politics)
Artificial intelligence
Security token
Embedding
Context (archaeology)
Word (group theory)
Benchmark (surveying)
Named-entity recognition
Similarity (geometry)
Scope (computer science)
Word embedding
Linguistics
Geodesy
Law
Politics
Task (project management)
Programming language
Computer security
Political science
Philosophy
Image (mathematics)
Management
Biology
Geography
Paleontology
Economics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1911.02257
- https://arxiv.org/pdf/1911.02257
- OA Status
- green
- Cited By
- 13
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2988560433
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2988560433Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1911.02257Digital Object Identifier
- Title
-
Hierarchical Contextualized Representation for Named Entity RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-11-06Full publication date if available
- Authors
-
Ying Luo, Fengshun Xiao, Hai ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/1911.02257Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1911.02257Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1911.02257Direct OA link when available
- Concepts
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Computer science, Sentence, Natural language processing, Representation (politics), Artificial intelligence, Security token, Embedding, Context (archaeology), Word (group theory), Benchmark (surveying), Named-entity recognition, Similarity (geometry), Scope (computer science), Word embedding, Linguistics, Geodesy, Law, Politics, Task (project management), Programming language, Computer security, Political science, Philosophy, Image (mathematics), Management, Biology, Geography, Paleontology, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 2, 2022: 2, 2021: 1, 2020: 4Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
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| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
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| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
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| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/1911.02257 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
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| primary_location.landing_page_url | http://arxiv.org/abs/1911.02257 |
| publication_date | 2019-11-06 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2963172229, https://openalex.org/W2998579922, https://openalex.org/W2798915520, https://openalex.org/W2963912736, https://openalex.org/W1940872118, https://openalex.org/W2626778328, https://openalex.org/W2880875857, https://openalex.org/W2963140597, https://openalex.org/W2250539671, https://openalex.org/W2409591106, https://openalex.org/W2185720331, https://openalex.org/W2949861626, https://openalex.org/W2963406669, https://openalex.org/W2998230451, https://openalex.org/W2147880316, https://openalex.org/W2308486447, https://openalex.org/W2798304389, https://openalex.org/W2957081657, https://openalex.org/W2963619022, https://openalex.org/W2952087486, https://openalex.org/W2963186636, https://openalex.org/W2971674945, https://openalex.org/W2964229180, https://openalex.org/W2891454293, https://openalex.org/W2296283641, https://openalex.org/W2948852532, https://openalex.org/W2787560479, https://openalex.org/W3037881859, https://openalex.org/W2756381707, https://openalex.org/W2963756980, https://openalex.org/W2891602716, https://openalex.org/W2946558277, https://openalex.org/W2963971244, https://openalex.org/W2896457183, https://openalex.org/W2963625095, https://openalex.org/W2103076621, https://openalex.org/W2962902328, https://openalex.org/W2950938254, https://openalex.org/W2293004735 |
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| abstract_inverted_index.a | 59, 80 |
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| abstract_inverted_index.models | 4 |
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| abstract_inverted_index.paper, | 51 |
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| abstract_inverted_index.Spanish | 159 |
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| abstract_inverted_index.dataset) | 160 |
| abstract_inverted_index.datasets | 151 |
| abstract_inverted_index.document | 47 |
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| abstract_inverted_index.results. | 167 |
| abstract_inverted_index.sentence | 82, 88 |
| abstract_inverted_index.(BiLSTM). | 14 |
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| abstract_inverted_index.augmented | 61 |
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| abstract_inverted_index.datasets, | 157 |
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| abstract_inverted_index.key-value | 103 |
| abstract_inverted_index.sensitive | 119 |
| abstract_inverted_index.sentence, | 41 |
| abstract_inverted_index.two-level | 126 |
| abstract_inverted_index.typically | 6 |
| abstract_inverted_index.(dataset). | 48 |
| abstract_inverted_index.CoNLL-2002 | 158 |
| abstract_inverted_index.mechanism. | 99 |
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| abstract_inverted_index.similarity | 121 |
| abstract_inverted_index.(CoNLL-2003 | 152 |
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| abstract_inverted_index.document-level, | 101 |
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| abstract_inverted_index.representation: | 65 |
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| abstract_inverted_index.sentence-level, | 72 |
| abstract_inverted_index.state-of-the-art | 166 |
| cited_by_percentile_year | |
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| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
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