Connections between the lines: augmenting social networks with text
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Summary
A novel probabilistic topic model is presented to analyze text corpora and infer descriptions of its entities and of relationships between those entities and it is shown qualitatively and quantitatively that the model can construct and annotate graphs of relationships and make useful predictions.
- Type
- article
- Published
- 2009-06-28
- Cited by
- 142
- References
- 37
- OpenAlex
- https://openalex.org/W2073792299
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:215806424
Keywords
Computer science, Encoding (memory), World Wide Web, Data science, Artificial intelligence
References
- Relationship Identification for Social Network Discovery
- An Introduction to Variational Methods for Graphical Models
- Topic and Role Discovery in Social Networks
- Discovering semantic biomedical relations utilizing the Web
- Mining hidden community in heterogeneous social networks
- Semantic annotation of frequent patterns
- Inferring Web communities from link topology
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
- GENETAG: a tagged corpus for gene/protein named entity recognition
- Structured entity identification and document categorization: two tasks with one joint model
- Logit models and logistic regressions for social networks: II. Multivariate relations.
- Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp
- Querying text databases for efficient information extraction
- Statistical entity-topic models
- Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining
- Influence and correlation in social networks
- Exploiting relational structure to understand publication patterns in high-energy physics
- Link Prediction in Relational Data
- Open Information Extraction from the Web
- Statistical properties of community structure in large social and information networks
Cited by
- An Approach to Incorporate Texts into a Social Network Analysis of Communication Graphs
- Applications of latent variable models in modeling influence and decision making
- Data Mining Algorithms for Internet Data: from Transport to Application Layer
- On Modeling Community Behaviors and Sentiments in Microblogging
- Mining Chat Logs to Extract Information about Authors and Topics for Crime Investigation
- From text to knowledge: bridging the gap with probabilistic graphical models
- Dynamic and Supervised Topic Models for Literature-Based Discovery
- Models, Inference, and Implementation for Scalable Probabilistic Models of Text
- Transforming Graph Data for Statistical Relational Learning
- Uncovering and Managing the Impact of Methodological Choices for the Computational Construction of Socio-Technical Networks from Texts
- Transforming Graph Representations for Statistical Relational Learning
- topicmodels: An R Package for Fitting Topic Models
- Knowledge Transfer Using Latent Variable Models
- Protecting attributes and contents in online social networks
- Extracting multi-dimensional relations: a generative model of groups of entities in a corpus
- Learning Classifiers from Distributional Data
- USN: An Optimization Framework for User-centric Social Networks
- Modeling topic control to detect influence in conversations using nonparametric topic models
- Group Profiling for Understanding Social Structures
- Analyzing topics and authors in chat logs for crime investigation
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