Bernoulli Process Topic Model for Citation Network Analysis
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Solution Overview
Problem
Existing topic models, such as LDA, fail to effectively capture the multi-level hierarchical structure of citation networks and the distinct roles of documents as both content providers and citations, leading to inadequate representation and analysis of document relationships.
Innovation Solution
The Bernoulli Process Topic (BPT) model explicitly models the topic distributions of documents at both document and citation levels, utilizing a Bernoulli process to capture the multi-level citation structure and incorporating citation information into the generative process, allowing for the differentiation of document roles and transitive citation relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing topic models (LDA, PLSI) are used to model documents, then the basic statistical information and latent topics can be extracted, but the multi-level hierarchical structure of citation networks and distinct document roles cannot be captured
Solution Approach 1:
The patent segments the document modeling process into two distinct levels: document level (where documents are modeled as mixtures of topics) and citation level (where cited documents are modeled separately). This segmentation allows the model to capture the multi-level hierarchical structure of citation networks by treating documents and citations as different entities with different topic distributions, thereby improving document representation accuracy without creating an intractably complex model.
Solution Approach 2:
The patent introduces a new dimension to the traditional single-level topic model by adding the citation level dimension. Instead of modeling only documents as mixtures of topics, the model now operates at two dimensions: the document level and the citation level. This dimensional expansion enables the capture of transitive citation relationships and distinct document roles while maintaining computational tractability through a systematic approach to modeling each level separately.
2Ease of manufacture
If documents are treated as independent in topic models, then the mathematical formulation becomes simple, but the actual relationships between documents (e.g., citations) are not captured
Solution Approach 1:
The patent segments the document collection into two distinct sets: documents and cited documents. By segmenting the data in this way, the model can maintain simple mathematical formulations for each level while capturing document relationships. The document level uses standard topic mixture modeling, while the citation level models the relationships between documents, thereby maintaining simplicity without sacrificing reliability.
Solution Approach 2:
The patent introduces an intermediary structure that connects the document level and citation level. The citation level acts as an intermediary that mediates between the simple document level modeling and the complex relationship capture. This intermediary layer allows the model to incorporate citation relationships systematically without complicating the overall formulation, as the citation level serves as a bridge that integrates both simple and complex aspects.
3Adaptability or versatility
If citations are treated as additional features similar to content, then the existing approaches can be applied, but the multi-level hierarchical structure and transitive citation relationships are not captured
Solution Approach 1:
The patent segments the treatment of citations into two distinct approaches: at the document level, citations are treated as additional features similar to content words, allowing existing topic models to be applied. At the citation level, however, the model captures the multi-level hierarchical structure and transitive relationships by modeling cited documents separately. This segmentation preserves the versatility of existing models while preventing the loss of citation structure information through dedicated citation-level modeling.
Solution Approach 2:
The patent adds another dimension to the feature space by introducing the citation level dimension. Instead of treating citations only as additional features at the document level, the model creates a separate dimension for citation-level modeling. This dimensional addition allows the capture of transitive citation relationships and hierarchical structure while maintaining the adaptability of existing models at the document level.
Data Source
AI summary
In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. A Bernoulli Process Topic (BPT) model is provided which models the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach.