Introduction to topic detection and tracking
By James Allan. 2002
336 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f775c64f9842a671bd4453dd13da556122633c28
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shaul Markovitch
- Evgeniy Gabrilovich
- Lillian Lee
- Bo Pang
- Behnam Hedayatnia
- Raefer Gabriel
- Kira Radinsky
- Luis Gravano
In Statistical and Nonlinear Physics
- Mónica Marrero
- Bo Pang
- Lillian Lee
- Julio Gonzalo
- Luis Gravano
- Eugene Agichtein
- Mor Naaman
- Enrique Amigó
Citations in the corpus
- Learning similarity metrics for event identification in social media (2010, background): “The topic detection and tracking (TDT) event detection task [2] was studied in a notable collective effort to discover and organize news events in a continuous stream (e.”
- Hip and trendy: Characterizing emerging trends on Twitter (2011, background): “In information retrieval, the concept of events has prominently been addressed in the area of topic detection in news events (Allan, 2002; Kleinberg, 2003; Yang, Pierce, & Carbonell, 1998).”
- Contextual Topic Modeling For Dialog Systems (2018, background): “An overview of classical approaches is provided in [15].”
- Learning similarity functions for topic detection in online reputation monitoring (2014, background): “The problem of Topic Detection and Tracking (TDT) in texts has been widely studied as event-based organization of newswire stories [1].”
- Opinion Mining and Sentiment Analysis (2008, background): “Such work is strongly related at a conceptual level to topic detection and tracking [8], a review of which is beyond the scope of this survey.”
- Explaining Credibility in News Articles using Cross-Referencing (2019, methodology): “Given a setAs of articles pertaining to the same story s (existing topic detection and tracking approaches can be employed to determine this set for each story [1]), our pipeline consists of three stages (see Figure 2): (1) text segmentation, (2) POI similarity, and (3) graph generation and clique l”
- A word at a time: computing word relatedness using temporal semantic analysis (2011, background): “Research on topic detection and tracking (TDT) analyzed the evolution of stories and topics over time [1].”