Explorations Within Topic Tracking and Detection

Allan, James; Lavrenko, Victor; Swan, Russell

doi:10.1007/978-1-4615-0933-2_10

Cited by 28 publications

(16 citation statements)

References 4 publications

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“…The temporal proximity parameter avoids clustering documents that are too far apart in time. Many different studies on event detection followed these initial initiatives (see [1] for the main approaches). Many of them rely on a vector space representation of the documents, where more recent approaches make a distinction between named entities and non named entity words (e.g., [12]).…”

Section: Related Researchmentioning

confidence: 99%

Cross-language linking of news stories on the web using interlingual topic modelling

Smet

Moens

2009

Proceedings of the 2nd ACM Workshop on Social Web Search and Mining

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We have studied the problem of linking event information across different languages without the use of translation systems or dictionaries. The linking is based on interlingua information obtained through probabilistic topic models trained on comparable corpora written in two languages (in our case English and Dutch). The achieve this, we expand the Latent Dirichlet Allocation model to process documents in two languages. We demonstrate the validity of the learned interlingual topics in a document clustering task, where the evaluation is performed on Google News.

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Section: Related Researchmentioning

confidence: 99%

Cross-language linking of news stories on the web using interlingual topic modelling

Smet

Moens

2009

Proceedings of the 2nd ACM Workshop on Social Web Search and Mining

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“…The community has produced several techniques for automatically clustering and detecting links between documents in a stream. Although more sophisticated language modeling systems have been developed for these tasks, the most successful approaches use straight-forward vectorspace techniques [2]. Nevertheless, language modeling systems provide a formal methodology for estimating the topic models.…”

Section: Related Literaturementioning

confidence: 99%

A User-Centered Approach to Evaluating Topic Models

Kelly¹,

Dı́az

Belkin

et al. 2004

Lecture Notes in Computer Science

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Abstract. This paper evaluates the automatic creation of personal topic models using two language model-based clustering techniques. The results of these methods are compared with user-defined topic classes of web pages from personal web browsing histories from a 5-week period. The histories and topics were gathered during a naturalistic case study of the online information search and use behavior of two users. This paper further investigates the effectiveness of using display time and retention behaviors as implicit evidence for weighting documents during topic model creation. Results show that agglomerative techniques -specifically, average-link clustering -provide the most effective methodology for building topic models while ignoring topic evidence and implicit evidence.

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“…In this form it bears strong resemblance to the lengthnormalized log-likelihood ratio, which has been used by a number of TDT participants [12,4]. Note that adding clarity has resulted in the denominator that plays a role similar to the role of idf in document retrieval.…”

Section: Measuring Topic Similaritymentioning

confidence: 99%

“…This method is the basis of everything from vector space approaches [4,17] to statistical language model [19,12] techniques. As in the field of Information Retrieval, much research focuses on techniques for selecting which words to compare, how they should be weighted, and how best to compare the sets of weighted words.…”

Section: Introductionmentioning

confidence: 99%

Relevance models for topic detection and tracking

Lavrenko

Allan

DeGuzman

et al. 2002

Proceedings of the Second International Conference on Human Language Technology Research -

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We extend relevance modeling to the link detection task of Topic Detection and Tracking (TDT) and show that it substantially improves performance. Relevance modeling, a statistical language modeling technique related to query expansion, is used to enhance the topic model estimate associated with a news story, boosting the probability of words that are associated with the story even when they do not appear in the story. To apply relevance modeling to TDT, it had to be extended to work with stories rather than short queries, and the similarity comparison had to be changed to a modified form of Kullback-Leibler. We demonstrate that relevance models result in very substantial improvements over the language modeling baseline. We also show how the use of relevance modeling makes it possible to choose a single parameter for within-and cross-mode comparisons of stories.

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Explorations Within Topic Tracking and Detection

Cited by 28 publications

References 4 publications

Cross-language linking of news stories on the web using interlingual topic modelling

Cross-language linking of news stories on the web using interlingual topic modelling

A User-Centered Approach to Evaluating Topic Models

Relevance models for topic detection and tracking

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