Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents
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Summary
A novel graphical model is introduced that can consider different types of prior knowledge about the task: within-document discourse patterns, cross-document sentence similarity information based on linguistic features, and prior knowledgeabout the correct classification of some of the input sentences when this information is available.
- Type
- article
- Published
- 2012-12-01
- Cited by
- 4
- References
- 43
- OpenAlex
- https://openalex.org/W2251028590
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8743015
Keywords
Computer science, Artificial intelligence, Argumentative, Natural language processing, Topic model
References
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- COGNITIVE MODELS OF WRITING: WRITING PROFICIENCY AS A COMPLEX INTEGRATED SKILL
- Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
- A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents
- Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users
- Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes
- Multi-Event Extraction Guided by Global Constraints
- Towards Domain-Independent Argumentative Zoning: Evidence from Chemistry and Computational Linguistics
- Extractive summarisation of legal texts
- Generative Content Models for Structural Analysis of Medical Abstracts
- Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
- Collective Information Extraction with Relational Markov Networks
- Learning by Transduction
Cited by
- Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints
- Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
- Social and Semantic Diversity: Socio-semantic Representation of a Scientific Corpus
- Unsupervised learning of rhetorical structure with un-topic models
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