Tracking dynamics of topic trends using a finite mixture model

Morinaga, Soichiro; Yamanishi, Kenji

doi:10.1145/1014052.1016919

Cited by 85 publications

(54 citation statements)

References 13 publications

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“…Others consider the influence among articles to be unidirectional and directly dependent, e.g. topic structure is identified in [25] by forgetting outof-date statistics, the bursty structure is recognized in [12] by estimating state transition probability in an infinite state automation.…”

Section: Text Summarization and Tdtmentioning

confidence: 99%

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Generating event storylines from microblogs

Lin

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on realtime events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

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Section: Text Summarization and Tdtmentioning

confidence: 99%

“…Similarly, a burstness-aware search framework is presented in [15]. A finite mixture model is presented in [25] for tracking dynamics of topic trends. ETS [36] returns the evolution skeleton along the timeline by extracting representative and discriminative sentences at each phase.…”

Section: Timeline and Storyline Constructionmentioning

confidence: 99%

Generating event storylines from microblogs

Lin

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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“…Most of them consider a xed feature space over the document stream: Morinaga and Yamanishi [12] use Expectation-Maximization to build soft clusters. A topic consists of the words with the largest information gain for a cluster.…”

Section: Related Workmentioning

confidence: 99%

Topic Evolution in a Stream of Documents

Gohr¹,

Hinneburg²,

Schult³

et al. 2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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Document collections evolve over time, new topics emerge and old ones decline. At the same time, the terminology evolves as well. Much literature is devoted to topic evolution in nite document sequences assuming a xed vocabulary. In this study, we propose \Topic Monitor" for the monitoring and understanding of topic and vocabulary evolution over an innite document sequence, i.e. a stream. We use Probabilistic Latent Semantic Analysis (PLSA) for topic modeling and propose new folding-in techniques for topic adaptation under an evolving vocabulary. We extract a series of models, on which we detect index-based topic threads as human-interpretable descriptions of topic evolution.

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“…Finite mixture models are used in [21] to represent the documents of each interval. A comparison of the models among subsequent time intervals is used to report new trends.…”

Section: Related Workmentioning

confidence: 99%

Anticipating annotations and emerging trends in biomedical literature

Mörchen

Dejori

Fradkin

et al. 2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeutic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-theart text mining technologies are deployed to provide added value on top of the original content, including named entity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of temporal dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the scientific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to annotate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long before they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease.

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Tracking dynamics of topic trends using a finite mixture model

Cited by 85 publications

References 13 publications

Generating event storylines from microblogs

Generating event storylines from microblogs

Topic Evolution in a Stream of Documents

Anticipating annotations and emerging trends in biomedical literature

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