Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats Article Swipe
Reshmi Ghosh
,
Harjeet Singh Kajal
,
Sharanya Kamath
,
Dhuri Shrivastava
,
Samyadeep Basu
,
Soundararajan Srinivasan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.14954
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.14954
Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often focus on segmentation of structured texts. In this paper, we comprehensively analyze the generalization capabilities of state-of-the-art topic segmentation models on unstructured texts. We find that: (a) Current strategies of pre-training on a large corpus of structured text such as Wiki-727K do not help in transferability to unstructured texts. (b) Training from scratch with only a relatively small-sized dataset of the target unstructured domain improves the segmentation results by a significant margin.
Related Topics
Concepts
Segmentation
Computer science
Margin (machine learning)
Focus (optics)
Natural language processing
Artificial intelligence
Conversation
Transferability
Unstructured data
Generalization
Structured prediction
Machine learning
Linguistics
Data mining
Big data
Mathematics
Optics
Mathematical analysis
Logit
Philosophy
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.14954
- https://arxiv.org/pdf/2211.14954
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310419376
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310419376Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.14954Digital Object Identifier
- Title
-
Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured ChatsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-27Full publication date if available
- Authors
-
Reshmi Ghosh, Harjeet Singh Kajal, Sharanya Kamath, Dhuri Shrivastava, Samyadeep Basu, Soundararajan SrinivasanList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.14954Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.14954Direct 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/2211.14954Direct OA link when available
- Concepts
-
Segmentation, Computer science, Margin (machine learning), Focus (optics), Natural language processing, Artificial intelligence, Conversation, Transferability, Unstructured data, Generalization, Structured prediction, Machine learning, Linguistics, Data mining, Big data, Mathematics, Optics, Mathematical analysis, Logit, Philosophy, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scratch | 89 |
| abstract_inverted_index.Breaking | 0 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.Training | 87 |
| abstract_inverted_index.document | 3 |
| abstract_inverted_index.improves | 101 |
| abstract_inverted_index.multiple | 8 |
| abstract_inverted_index.segments | 10 |
| abstract_inverted_index.semantic | 14 |
| abstract_inverted_index.Wiki-727K | 77 |
| abstract_inverted_index.important | 18 |
| abstract_inverted_index.structure | 15 |
| abstract_inverted_index.contiguous | 9 |
| abstract_inverted_index.downstream | 28 |
| abstract_inverted_index.relatively | 93 |
| abstract_inverted_index.strategies | 65 |
| abstract_inverted_index.structured | 41, 73 |
| abstract_inverted_index.challenging | 20 |
| abstract_inverted_index.significant | 107 |
| abstract_inverted_index.small-sized | 94 |
| abstract_inverted_index.capabilities | 51 |
| abstract_inverted_index.conversation | 6 |
| abstract_inverted_index.pre-training | 67 |
| abstract_inverted_index.segmentation | 35, 39, 55, 103 |
| abstract_inverted_index.unstructured | 58, 84, 99 |
| abstract_inverted_index.generalization | 50 |
| abstract_inverted_index.comprehensively | 47 |
| abstract_inverted_index.transferability | 82 |
| abstract_inverted_index.state-of-the-art | 53 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |