Topic Model

转载自 flyer_hit

最终编辑 pimpking

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