Topic Model 都有哪些

Topic model

Content:

• basic topic model: PLSA, LDA
  • Mining multi-faceted overviews of arbitrary topics in a text collection
  • Modeling online reviews with multi-grain topic models
  • Multiscale topic tomography
• NLP
  • A topic model for word sense disambiguationSyntactic topic models
  • Integrating topics and syntax
  • Topic modeling: beyond bag-of-words
  • A Bayesian LDA-based model for semi-supervised part-of-speech tagging
  • Topical n-grams: Phrase and topic discovery, with an application to information retrieval
  • A topic model for word sense disambiguation

• opinion mining
  • Topic sentiment mixture: modeling facets and opinions in weblogs
  • A joint model of text and aspect ratings for sentiment summarization
  • Learning document-level semantic properties from free-text annotations
  • Opinion integration through semi-supervised topic modeling
  • ARSA: a sentiment-aware model for predicting sales performance using blogs
  • Joint Sentiment/Topic Model for Sentiment Analysis

• retrieval
  • LDA-based document models for ad-hoc retrieval
  • Exploring social annotations for information retrieval
  • Modeling general and specific aspects of documents with a probabilistic topic model
  • Exploring topic-based language models for effective web information retrieval
  • Probabilistic Models for Expert Finding
• topic labeling
  • Generating summary keywords for emails using topics
  • Automatic Labeling of Multinomial Topic Models
  • Semantic Annotation of Frequent Patterns
• spam filtering
  • Latent dirichlet allocation in web spam filtering
  • Linked latent dirichlet allocation in web spam filtering
• topic segmentation
  • Topic-based document segmentation with probabilistic latent semantic analysis
  • Bayesian unsupervised topic segmentation
  • Text segmentation with LDA-based Fisher kernel
  • Hierarchical text segmentation from multi-scale lexical cohesion
  • Extraction of coherent relevant passages using hidden Markov models
  • Topic segmentation with an aspect hidden Markov model
  • Detecting Topic Drift with Compound Topic Models
• information extraction
  • Employing Topic Models for Pattern-based Semantic Class Discovery
  • Combining Concept Hierarchies and Statistical Topic Models
  • A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering New Attributes
  • An Unsupervised Framework for Extracting and Normalizing Product Attributes from Multiple Web Sites
  • Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach
  • Adapting Web Information Extraction Knowledge via Mining Site Invariant and Site Dependent Features
  • Learning to Extract and Summarize Hot Item Features from Multiple Auction Web Sites"
  • Semi-supervised Extraction of Entity Aspects Using Topic Models
• summarization
  • Bayesian query-focused summarization
  • Topic-based multi-document summarization with probabilistic latent semantic analysis
  • Multi-topic based Query-oriented Summarization
  • Multi-Document Summarization using Sentence-based Topic Models
  • Generating Impact-Based Summaries for Scientific Literature
  • Generating Comparative Summaries of Contradictory Opinions in Text
  • Rated Aspect Summarization of Short Comments
• collaborative filtering
  • Latent semantic models for collaborative filtering
  • Google news personalization: scalable online collaborative filtering
  • Combinational collaborative filtering for personalized community recommendation
  • Latent dirichlet allocation for tag recommendation
  • Time-Sensitive Language Modelling for Online Term Recurrence Prediction
  • Tag-LDA for Scalable Real-time Tag Recommendation

Temporal factor

• dynamic topic model
  • Dynamic topic models
  • A probabilistic approach to spatiotemporal theme pattern mining on weblogs
  • Continuous time dynamic topic models
  • Dynamic mixture models for multiple time series
  • On-Line LDA: Adaptive Topic Models for Mining Text Streams
  • Topic models over text streams: A study of batch and online unsupervised learning

• event mining & theme evolution & text stream mining
  • Discovering evolutionary theme patterns from text: an exploration of temporal text mining
  • Topics over time: a non-markov continuous-time model of topical trends
  • Topic models over text streams: A study of batch and online unsupervised learning
  • Mining correlated bursty topic patterns from coordinated text streams
  • Topic Evolution in a stream of Documents

Entity:

• Author-topic model & citation research & review match
  • The author-topic model for authors and documents
  • Probabilistic author-topic models for information discovery
  • The author-recipient-topic model for topic and role discovery in social networks
  • Expertise modeling for matching papers with reviewers
  • Topic evolution and social interactions: how authors effect research
  • Joint latent topic models for text and citations
  • Co-ranking authors and documents in a heterogeneous network
  • Mixed-membership models of scientific publications
  • Modeling individual differences using Dirichlet processes
  • Multi-aspect expertise matching for review assignment
  • Topic-link LDA: joint models of topic and author community
  • Group and topic discovery from relations and their attributes
  • Exploiting Temporal Authors Interests via Temporal-Author-Topic Modeling, ADMA 2009
  • Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation, ECIR 2009

Network:

• entity-topic model
  • Statistical entity-topic models
  • Named entity recognition in query
• link entity
  • Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs
  • Connections between the lines: augmenting social networks with text
  • Relational topic models for document networks

• community discovery
  • Topic and role discovery in social networks with experiments on enron and academic email
  • Group and topic discovery from relations and text
  • Probabilistic models for discovering e-communities
  • Arnetminer: Extraction and mining of academic social networks
  • Community evolution in dynamic multi-mode networks
  • An LDA-based community structure discovery approach for large-scale social networks
  • Probabilistic community discovery using hierarchical latent gaussian mixture model
  • Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation
  • Joint group and topic discovery from relations and text
  • Social topic models for community extraction
  • Combining link and content for community detection: a discriminative approach
  • Topic-Link LDA: Joint Models of Topic and Author Community

• network regularization
  • Modeling hidden topics on document manifold
  • Topic Modeling with Network Regularization

• Evaluation
• Reading tea leaves: How humans interpret topic models