Diversified Hidden Markov Models for Sequential Labeling Article Swipe
Maoying Qiao
,
Wei Bian
,
Richard Yi Da Xu
,
Dacheng Tao
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2015.2433262
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2015.2433262
Labeling of sequential data is a prevalent meta-problem for a wide range of\nreal world applications. While the first-order Hidden Markov Models (HMM)\nprovides a fundamental approach for unsupervised sequential labeling, the basic\nmodel does not show satisfying performance when it is directly applied to real\nworld problems, such as part-of-speech tagging (PoS tagging) and optical\ncharacter recognition (OCR). Aiming at improving performance, important\nextensions of HMM have been proposed in the literatures. One of the common key\nfeatures in these extensions is the incorporation of proper prior information.\nIn this paper, we propose a new extension of HMM, termed diversified Hidden\nMarkov Models (dHMM), which utilizes a diversity-encouraging prior over the\nstate-transition probabilities and thus facilitates more dynamic sequential\nlabellings. Specifically, the diversity is modeled by a continuous\ndeterminantal point process prior, which we apply to both unsupervised and\nsupervised scenarios. Learning and inference algorithms for dHMM are derived.\nEmpirical evaluations on benchmark datasets for unsupervised PoS tagging and\nsupervised OCR confirmed the effectiveness of dHMM, with competitive\nperformance to the state-of-the-art.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tkde.2015.2433262
- OA Status
- green
- Cited By
- 25
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W1675155851Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tkde.2015.2433262Digital Object Identifier
- Title
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Diversified Hidden Markov Models for Sequential LabelingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-05-14Full publication date if available
- Authors
-
Maoying Qiao, Wei Bian, Richard Yi Da Xu, Dacheng TaoList of authors in order
- Landing page
-
https://doi.org/10.1109/tkde.2015.2433262Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1904.03170Direct OA link when available
- Concepts
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Computer science, Hidden Markov model, Markov process, Markov chain, Markov model, Artificial intelligence, Machine learning, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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25Total citation count in OpenAlex
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2024: 5, 2023: 1, 2022: 2, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
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79Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6682334925, https://openalex.org/W2125838338, https://openalex.org/W2330149175, https://openalex.org/W6636649193, https://openalex.org/W2013988526, https://openalex.org/W2171080222, https://openalex.org/W6635494071, https://openalex.org/W2465027326, https://openalex.org/W2099529148, https://openalex.org/W2149551320, https://openalex.org/W2015245929, https://openalex.org/W2045344178, https://openalex.org/W6683465734, https://openalex.org/W6678251536, https://openalex.org/W6683355481, https://openalex.org/W6675002571, https://openalex.org/W1958800402, https://openalex.org/W2159428510, https://openalex.org/W6682443497, https://openalex.org/W2084028080, https://openalex.org/W1998683369, https://openalex.org/W2138779671, https://openalex.org/W6636104918, https://openalex.org/W2614053927, https://openalex.org/W6680911227, https://openalex.org/W2093989924, https://openalex.org/W6638584940, https://openalex.org/W6679853412, https://openalex.org/W2047758746, https://openalex.org/W6638384976, https://openalex.org/W6638914580, https://openalex.org/W1517708896, https://openalex.org/W7024135352, https://openalex.org/W6910383664, https://openalex.org/W2148753264, https://openalex.org/W6688880502, https://openalex.org/W2152770371, https://openalex.org/W6679559908, https://openalex.org/W2158266063, https://openalex.org/W6686821501, https://openalex.org/W6675760969, https://openalex.org/W6682418119, https://openalex.org/W2158520534, https://openalex.org/W2099873701, https://openalex.org/W1592736283, https://openalex.org/W2222269984, https://openalex.org/W2962775423, https://openalex.org/W2953102581, https://openalex.org/W1601795611, https://openalex.org/W2158334272, https://openalex.org/W1632114991, https://openalex.org/W2105644991, https://openalex.org/W1663973292, https://openalex.org/W4253573210, https://openalex.org/W1570122015, https://openalex.org/W2134553104, https://openalex.org/W3003413895, https://openalex.org/W2890912593, https://openalex.org/W2964105469, https://openalex.org/W2187580259, https://openalex.org/W2153412625, https://openalex.org/W2154090186, https://openalex.org/W2131029730, https://openalex.org/W1570013475, https://openalex.org/W2151531457, https://openalex.org/W3103014337, https://openalex.org/W2177179288, https://openalex.org/W1603660271, https://openalex.org/W2963533033, https://openalex.org/W2148516480, https://openalex.org/W2123068277, https://openalex.org/W1818395637, https://openalex.org/W2141425367, https://openalex.org/W2150142469, https://openalex.org/W2123167923, https://openalex.org/W1594523940, https://openalex.org/W1820075673, https://openalex.org/W1856231595, https://openalex.org/W1560009468 |
| referenced_works_count | 79 |
| abstract_inverted_index.a | 5, 9, 22, 86, 98, 116 |
| abstract_inverted_index.as | 45 |
| abstract_inverted_index.at | 55 |
| abstract_inverted_index.by | 115 |
| abstract_inverted_index.in | 64, 72 |
| abstract_inverted_index.is | 4, 38, 75, 113 |
| abstract_inverted_index.it | 37 |
| abstract_inverted_index.of | 1, 59, 68, 78, 89, 150 |
| abstract_inverted_index.on | 138 |
| abstract_inverted_index.to | 41, 124, 154 |
| abstract_inverted_index.we | 84, 122 |
| abstract_inverted_index.HMM | 60 |
| abstract_inverted_index.OCR | 146 |
| abstract_inverted_index.One | 67 |
| abstract_inverted_index.PoS | 143 |
| abstract_inverted_index.and | 50, 104, 130 |
| abstract_inverted_index.are | 135 |
| abstract_inverted_index.for | 8, 25, 133, 141 |
| abstract_inverted_index.new | 87 |
| abstract_inverted_index.not | 32 |
| abstract_inverted_index.the | 16, 29, 65, 69, 76, 111, 148, 155 |
| abstract_inverted_index.(PoS | 48 |
| abstract_inverted_index.HMM, | 90 |
| abstract_inverted_index.been | 62 |
| abstract_inverted_index.both | 125 |
| abstract_inverted_index.dHMM | 134 |
| abstract_inverted_index.data | 3 |
| abstract_inverted_index.does | 31 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.more | 107 |
| abstract_inverted_index.over | 101 |
| abstract_inverted_index.show | 33 |
| abstract_inverted_index.such | 44 |
| abstract_inverted_index.this | 82 |
| abstract_inverted_index.thus | 105 |
| abstract_inverted_index.when | 36 |
| abstract_inverted_index.wide | 10 |
| abstract_inverted_index.with | 152 |
| abstract_inverted_index.While | 15 |
| abstract_inverted_index.apply | 123 |
| abstract_inverted_index.dHMM, | 151 |
| abstract_inverted_index.point | 118 |
| abstract_inverted_index.prior | 80, 100 |
| abstract_inverted_index.range | 11 |
| abstract_inverted_index.these | 73 |
| abstract_inverted_index.which | 96, 121 |
| abstract_inverted_index.world | 13 |
| abstract_inverted_index.(OCR). | 53 |
| abstract_inverted_index.Aiming | 54 |
| abstract_inverted_index.Hidden | 18 |
| abstract_inverted_index.Markov | 19 |
| abstract_inverted_index.Models | 20, 94 |
| abstract_inverted_index.common | 70 |
| abstract_inverted_index.paper, | 83 |
| abstract_inverted_index.prior, | 120 |
| abstract_inverted_index.proper | 79 |
| abstract_inverted_index.termed | 91 |
| abstract_inverted_index.(dHMM), | 95 |
| abstract_inverted_index.applied | 40 |
| abstract_inverted_index.dynamic | 108 |
| abstract_inverted_index.modeled | 114 |
| abstract_inverted_index.process | 119 |
| abstract_inverted_index.propose | 85 |
| abstract_inverted_index.tagging | 47, 144 |
| abstract_inverted_index.Labeling | 0 |
| abstract_inverted_index.Learning | 129 |
| abstract_inverted_index.approach | 24 |
| abstract_inverted_index.datasets | 140 |
| abstract_inverted_index.directly | 39 |
| abstract_inverted_index.of\nreal | 12 |
| abstract_inverted_index.proposed | 63 |
| abstract_inverted_index.tagging) | 49 |
| abstract_inverted_index.utilizes | 97 |
| abstract_inverted_index.benchmark | 139 |
| abstract_inverted_index.confirmed | 147 |
| abstract_inverted_index.diversity | 112 |
| abstract_inverted_index.extension | 88 |
| abstract_inverted_index.improving | 56 |
| abstract_inverted_index.inference | 131 |
| abstract_inverted_index.labeling, | 28 |
| abstract_inverted_index.prevalent | 6 |
| abstract_inverted_index.problems, | 43 |
| abstract_inverted_index.algorithms | 132 |
| abstract_inverted_index.extensions | 74 |
| abstract_inverted_index.satisfying | 34 |
| abstract_inverted_index.scenarios. | 128 |
| abstract_inverted_index.sequential | 2, 27 |
| abstract_inverted_index.diversified | 92 |
| abstract_inverted_index.evaluations | 137 |
| abstract_inverted_index.facilitates | 106 |
| abstract_inverted_index.first-order | 17 |
| abstract_inverted_index.fundamental | 23 |
| abstract_inverted_index.performance | 35 |
| abstract_inverted_index.real\nworld | 42 |
| abstract_inverted_index.recognition | 52 |
| abstract_inverted_index.basic\nmodel | 30 |
| abstract_inverted_index.literatures. | 66 |
| abstract_inverted_index.meta-problem | 7 |
| abstract_inverted_index.performance, | 57 |
| abstract_inverted_index.unsupervised | 26, 126, 142 |
| abstract_inverted_index.Specifically, | 110 |
| abstract_inverted_index.applications. | 14 |
| abstract_inverted_index.effectiveness | 149 |
| abstract_inverted_index.incorporation | 77 |
| abstract_inverted_index.key\nfeatures | 71 |
| abstract_inverted_index.probabilities | 103 |
| abstract_inverted_index.Hidden\nMarkov | 93 |
| abstract_inverted_index.part-of-speech | 46 |
| abstract_inverted_index.(HMM)\nprovides | 21 |
| abstract_inverted_index.and\nsupervised | 127, 145 |
| abstract_inverted_index.information.\nIn | 81 |
| abstract_inverted_index.optical\ncharacter | 51 |
| abstract_inverted_index.derived.\nEmpirical | 136 |
| abstract_inverted_index.state-of-the-art.\n | 156 |
| abstract_inverted_index.diversity-encouraging | 99 |
| abstract_inverted_index.important\nextensions | 58 |
| abstract_inverted_index.the\nstate-transition | 102 |
| abstract_inverted_index.sequential\nlabellings. | 109 |
| abstract_inverted_index.competitive\nperformance | 153 |
| abstract_inverted_index.continuous\ndeterminantal | 117 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.8513044 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |