Context Reinforced Neural Topic Modeling over Short Texts Article Swipe

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Jiachun Feng , Zusheng Zhang , Cheng Ding , Yanghui Rao , Haoran Xie ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2008.04545

As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities. However, due to the lack of context in each short text, the existing neural topic models may suffer from feature sparsity on such documents. To alleviate this issue, we propose a Context Reinforced Neural Topic Model (CRNTM), whose characteristics can be summarized as follows. Firstly, by assuming that each short text covers only a few salient topics, CRNTM infers the topic for each word in a narrow range. Secondly, our model exploits pre-trained word embeddings by treating topics as multivariate Gaussian distributions or Gaussian mixture distributions in the embedding space. Extensive experiments on two benchmark datasets validate the effectiveness of the proposed model on both topic discovery and text classification.

Related Topics

Computer Science

Benchmark (Surveying)

Artificial Intelligence

Aerospace Engineering

Concepts

Computer science Context (archaeology) Salient Word (group theory) Benchmark (surveying) Artificial intelligence Feature (linguistics) Topic model Embedding Exploit Range (aeronautics) Word embedding Multivariate statistics Key (lock) Machine learning Natural language processing Gaussian Space (punctuation) Mathematics Linguistics Engineering Geometry Biology Geodesy Geography Physics Quantum mechanics Aerospace engineering Operating system Paleontology Computer security Philosophy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2008.04545
PDF: https://arxiv.org/pdf/2008.04545
OA Status: green
Cited By: 3
Related Works: 10
OpenAlex ID: https://openalex.org/W4320878907

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DOI: https://doi.org/10.48550/arxiv.2008.04545

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Title: Context Reinforced Neural Topic Modeling over Short Texts

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-08-11

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Authors: Jiachun Feng, Zusheng Zhang, Cheng Ding, Yanghui Rao, Haoran Xie

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Landing page: https://arxiv.org/abs/2008.04545

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PDF URL: https://arxiv.org/pdf/2008.04545

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Concepts: Computer science, Context (archaeology), Salient, Word (group theory), Benchmark (surveying), Artificial intelligence, Feature (linguistics), Topic model, Embedding, Exploit, Range (aeronautics), Word embedding, Multivariate statistics, Key (lock), Machine learning, Natural language processing, Gaussian, Space (punctuation), Mathematics, Linguistics, Engineering, Geometry, Biology, Geodesy, Geography, Physics, Quantum mechanics, Aerospace engineering, Operating system, Paleontology, Computer security, Philosophy

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Cited by: 3

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Citations by year (recent): 2023: 2, 2022: 1

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Related works (count): 10

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abstract_inverted_index.one	1
abstract_inverted_index.our	98
abstract_inverted_index.the	3, 18, 32, 40, 88, 116, 126, 129
abstract_inverted_index.two	122
abstract_inverted_index.both	133
abstract_inverted_index.each	37, 77, 91
abstract_inverted_index.from	47
abstract_inverted_index.high	21
abstract_inverted_index.lack	33
abstract_inverted_index.only	81
abstract_inverted_index.such	51
abstract_inverted_index.text	79, 137
abstract_inverted_index.that	76
abstract_inverted_index.this	55
abstract_inverted_index.word	92, 102
abstract_inverted_index.CRNTM	86
abstract_inverted_index.Model	64
abstract_inverted_index.Topic	63
abstract_inverted_index.model	99, 131
abstract_inverted_index.short	38, 78
abstract_inverted_index.text,	39
abstract_inverted_index.topic	5, 9, 43, 89, 134
abstract_inverted_index.whose	66
abstract_inverted_index.Neural	62
abstract_inverted_index.covers	80
abstract_inverted_index.infers	87
abstract_inverted_index.issue,	56
abstract_inverted_index.mining	6
abstract_inverted_index.models	44
abstract_inverted_index.narrow	95
abstract_inverted_index.neural	8, 42
abstract_inverted_index.range.	96
abstract_inverted_index.space.	118
abstract_inverted_index.strong	26
abstract_inverted_index.suffer	46
abstract_inverted_index.tools,	7
abstract_inverted_index.topics	106
abstract_inverted_index.Context	60
abstract_inverted_index.context	35
abstract_inverted_index.feature	48
abstract_inverted_index.mixture	113
abstract_inverted_index.propose	58
abstract_inverted_index.salient	84
abstract_inverted_index.topics,	85
abstract_inverted_index.(CRNTM),	65
abstract_inverted_index.Firstly,	73
abstract_inverted_index.Gaussian	109, 112
abstract_inverted_index.However,	29
abstract_inverted_index.assuming	75
abstract_inverted_index.datasets	124
abstract_inverted_index.existing	41
abstract_inverted_index.exploits	100
abstract_inverted_index.follows.	72
abstract_inverted_index.modeling	10
abstract_inverted_index.proposed	130
abstract_inverted_index.sparsity	49
abstract_inverted_index.training	24
abstract_inverted_index.treating	105
abstract_inverted_index.validate	125
abstract_inverted_index.Extensive	119
abstract_inverted_index.Secondly,	97
abstract_inverted_index.alleviate	54
abstract_inverted_index.attracted	12
abstract_inverted_index.benchmark	123
abstract_inverted_index.discovery	135
abstract_inverted_index.embedding	117
abstract_inverted_index.interests	16
abstract_inverted_index.prevalent	4
abstract_inverted_index.Reinforced	61
abstract_inverted_index.abilities.	28
abstract_inverted_index.advantages	19
abstract_inverted_index.documents.	52
abstract_inverted_index.efficiency	22
abstract_inverted_index.embeddings	103
abstract_inverted_index.summarized	70
abstract_inverted_index.experiments	120
abstract_inverted_index.pre-trained	101
abstract_inverted_index.multivariate	108
abstract_inverted_index.distributions	110, 114
abstract_inverted_index.effectiveness	127
abstract_inverted_index.generalisation	27
abstract_inverted_index.characteristics	67
abstract_inverted_index.classification.	138
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile