Topic detection and tracking: event-based information organization
By James Allan. 2002
938 citations recorded by the source corpus.
Subfields: Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Jimmy J. Lin
- Rodrigo Nogueira
- Zhiyuan Liu
- Andrew Yates
- Xu Han
- Miles Efron
- Mark Sanderson
- Garrick Sherman
In Evaluation & Benchmarks
- Jimmy J. Lin
- Mark Sanderson
- Rodrigo Nogueira
- Andrew Yates
- Zhiyuan Liu
- Garrick Sherman
- Yulu Wang
- Miles Efron
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “n the context of the TREC Filtering Tracks, which ran from 1995 [Lewis, 1995] to 2002 [Robertson and Soboroff, 2002], and the general research area of topic detection and tracking, also known as TDT [Allan, 2002]. The relationship between search and filtering has been noted for decades: Belkin and Cr”
- Processing Social Media Messages in Mass Emergency (2014, background): “For a general discussion on the subject, see [Allan 2002; Lee et al. 2013].”
- Assessor Differences and User Preferences in Tweet Timeline Generation (2015, background): “At a high level, the tweet timeline generation task bears a family resemblance to topic detection and tracking (TDT) [33, 3], “other” nuggets in question answering [32, 9, 8], nugget-based evaluations in DARPA’s BOLT program, 1 and temporal summarization at TREC [12, 5].”
- Real-Time Detection of Traffic From Twitter Stream Analysis (2015, background): “An event can be defined as a real-world occurrence that happens in a specific time and space [1], [7].”
- Hip and trendy: Characterizing emerging trends on Twitter (2011, background): “In subsequent sections we qualitatively examine a subset TQual of the trends in T to extract the key types of trends that are present in Twitter data and develop a set of dimensions according to which trends can be categorized.”
- Information diffusion through blogspace (2004, background): “The field of topic detection and trackinghas studied this problem in depth for a number of years—NIST has run a series of workshops and open evaluation challenges [30]; see also, for example, [3].”
- Emerging topic detection on Twitter based on temporal and social terms evaluation (2010, background): “It tells people what they care about as it is happening in the world.”
- Toponym resolution in text: annotation, evaluation and applications of spatial grounding (2007, background): “• Better event detection/tracking and clustering (Allan (2002)): if events are predications that hold in a space over a period of time, the reliable spatial grounding of an event should provide us with a solid basis for identifying events, helping not only to detect identical events, but also to ord”
- Streaming First Story Detection with application to Twitter (2010, background): “New event detection, also known as first story detection (FSD)(1) is defined within the topic detection and tracking as one of the subtasks (Allan, 2002).”
- Retrieval and novelty detection at the sentence level (2003, background): “A significant body of related work arises from the Topic Detection and Tracking (TDT) research and evaluation project, which is concerned with online event detection and tracking in news stories [1, 2, 5, 10, 11, 13].”
- TextFlow: Towards Better Understanding of Evolving Topics in Text (2011, background): “Topic detection and tracking [1] [33] aims to automatically detect new topics and find new stories on already known topics in temporallyordered news streams.”