Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying Article Swipe
Xianbin Hong
,
Gautam Pal
,
Sheng-Uei Guan
,
Prudence W. H. Wong
,
Dawei Liu
,
Ka Lok Man
,
Xin Huang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.01988
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.01988
Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Naïve Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1905.01988
- https://arxiv.org/pdf/1905.01988
- OA Status
- green
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2947318369
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2947318369Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1905.01988Digital Object Identifier
- Title
-
Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-StudyingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-04-30Full publication date if available
- Authors
-
Xianbin Hong, Gautam Pal, Sheng-Uei Guan, Prudence W. H. Wong, Dawei Liu, Ka Lok Man, Xin HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/1905.01988Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1905.01988Direct 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/1905.01988Direct OA link when available
- Concepts
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Annotation, Lifelong learning, Computer science, Artificial intelligence, Unsupervised learning, Natural language processing, Psychology, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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8Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 94 |
| abstract_inverted_index.also | 86 |
| abstract_inverted_index.best | 49 |
| abstract_inverted_index.cost | 112 |
| abstract_inverted_index.data | 83, 109 |
| abstract_inverted_index.even | 121 |
| abstract_inverted_index.less | 107 |
| abstract_inverted_index.like | 35 |
| abstract_inverted_index.only | 44 |
| abstract_inverted_index.some | 39 |
| abstract_inverted_index.than | 123 |
| abstract_inverted_index.that | 97 |
| abstract_inverted_index.them | 74 |
| abstract_inverted_index.this | 62 |
| abstract_inverted_index.upon | 51 |
| abstract_inverted_index.well | 118 |
| abstract_inverted_index.with | 90 |
| abstract_inverted_index.Bayes | 37 |
| abstract_inverted_index.based | 42 |
| abstract_inverted_index.novel | 5 |
| abstract_inverted_index.paper | 63, 95 |
| abstract_inverted_index.solve | 28 |
| abstract_inverted_index.task. | 54 |
| abstract_inverted_index.them, | 56 |
| abstract_inverted_index.which | 9 |
| abstract_inverted_index.Naïve | 36 |
| abstract_inverted_index.Unlike | 55 |
| abstract_inverted_index.better | 122 |
| abstract_inverted_index.demand | 80 |
| abstract_inverted_index.during | 14, 70 |
| abstract_inverted_index.enable | 22 |
| abstract_inverted_index.neural | 40 |
| abstract_inverted_index.single | 53 |
| abstract_inverted_index.tasks. | 77 |
| abstract_inverted_index.achieve | 47, 114 |
| abstract_inverted_index.focuses | 64 |
| abstract_inverted_index.further | 76 |
| abstract_inverted_index.machine | 1, 6, 25, 59 |
| abstract_inverted_index.network | 41 |
| abstract_inverted_index.related | 30 |
| abstract_inverted_index.reusing | 20 |
| abstract_inverted_index.Lifelong | 0 |
| abstract_inverted_index.labelled | 82, 108 |
| abstract_inverted_index.learning | 2, 7, 26, 60, 71, 103 |
| abstract_inverted_index.leverage | 73 |
| abstract_inverted_index.lifelong | 24, 58, 102 |
| abstract_inverted_index.paradigm | 8 |
| abstract_inverted_index.reusing. | 93 |
| abstract_inverted_index.suggests | 96 |
| abstract_inverted_index.training | 85 |
| abstract_inverted_index.abilities | 21 |
| abstract_inverted_index.decreased | 89 |
| abstract_inverted_index.knowledge | 13, 17, 69, 92 |
| abstract_inverted_index.learning. | 15, 126 |
| abstract_inverted_index.problems. | 31 |
| abstract_inverted_index.Meanwhile, | 78 |
| abstract_inverted_index.accumulate | 12, 68 |
| abstract_inverted_index.approaches | 34, 43 |
| abstract_inverted_index.extracting | 18 |
| abstract_inverted_index.supervised | 125 |
| abstract_inverted_index.continually | 11 |
| abstract_inverted_index.performance | 50, 116 |
| abstract_inverted_index.traditional | 33 |
| abstract_inverted_index.computational | 111 |
| abstract_inverted_index.significantly | 88 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |