Introduction to Topic Detection and Tracking

Allan, James

doi:10.1007/978-1-4615-0933-2_1

Cited by 238 publications

(222 citation statements)

References 5 publications

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“…[13], Section 2.5.2). 2 The logical or modal dependencies between these sub-DRSs are then expressed by means of additional fixed vocabulary in the namespace reify:. This results in flat RDF output for nested DRSs.…”

Section: Generating An Rdf Graphmentioning

confidence: 99%

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LODifier: Generating Linked Data from Unstructured Text

Augenstein

Padó

Rudolph

2012

Lecture Notes in Computer Science

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Abstract. The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential.

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Section: Generating An Rdf Graphmentioning

confidence: 99%

“…More specifically, we consider a story link detection task, part of the topic detection and tracking (TDT) family of tasks. It is defined as "[...] the problem of deciding whether two randomly selected stories discuss the same news topic" [2]. We use a subset of the TDT-2 benchmark dataset.…”

Section: Task and Setupmentioning

confidence: 99%

LODifier: Generating Linked Data from Unstructured Text

Augenstein

Padó

Rudolph

2012

Lecture Notes in Computer Science

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“…Information landscapes are commonly used to visualize topical relatedness in large document repositories, for example in Krishnan et al [20] and Andrews et al [1]. Static landscape visualizations, however, cannot convey changes.…”

Section: Related Workmentioning

confidence: 99%

“…In the late nineties, several incremental clustering algorithms have been presented including BIRCH [35], incremental DBSCAN [8] to support data warehousing or Ribert et al's clustering algorithm to generate a hierarchy of clusters [26]. Incremental clustering of text documents has been conducted as a part of the Topic Detection and Tracking initiative [1] to detect a new event from a stream of news articles.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Syed

Kroll

Sabol

et al. 2012

Data Warehousing and Knowledge Discovery

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Abstract. Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.

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“…To detect the appearance of new topics and tracking the reappearance and evolution of them is the goal of topic detection and tracking [2,1]. For a collection of documents, relevant terms need to be identified and related to a particular time-span, or known events, and vice versa, time-spans need to be related to relevant terms.…”

Section: Introductionmentioning

confidence: 99%

Towards Automatic Detection and Tracking of Topic Change

Holz

Teresniak

2010

Computational Linguistics and Intelligent Text Processing

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Abstract. We present an approach for automatic detection of topic change. Our approach is based on the analysis of statistical features of topics in time-sliced corpora and their dynamics over time. Processing large amounts of time-annotated news text, we identify new facets regarding a stream of topics consisting of latest news of public interest. Adaptable as an addition to the well known task of topic detection and tracking we aim to boil down a daily news stream to its novelty. For that we examine the contextual shift of the concepts over time slices. To quantify the amount of change, we adopt the volatility measure from econometrics and propose a new algorithm for frequency-independent detection of topic drift and change of meaning. The proposed measure does not rely on plain word frequency but the mixture of the co-occurrences of words. So, the analysis is highly independent of the absolute word frequencies and works over the whole frequency spectrum, especially also well for low-frequent words. Aggregating the computed time-related data of the terms allows to build overview illustrations of the most evolving terms for a whole time span.

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Introduction to Topic Detection and Tracking

Cited by 238 publications

References 5 publications

LODifier: Generating Linked Data from Unstructured Text

LODifier: Generating Linked Data from Unstructured Text

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Towards Automatic Detection and Tracking of Topic Change

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