Retrospective Event Detection Research Articles

Unified approach to retrospective event detection for event- based epidemic intelligence.

Inferring the magnitude and occurrence of real-world events from natural language text is a crucial task in various domains. Particularly in the domain of public health, the state-of-the-art document and token centric event detection approaches have not kept the pace with the growing need for more robust event detection in public health. In this paper, we propose UPHED, a unified approach, which combines both the document and token centric event detection techniques in an unsupervised manner such that events which are: rare (aperiodic); reoccurring (periodic) can be detected using a generative model for the domain of public health. We evaluate the efficiency of our approach as well as its effectiveness for two real-world case studies with respect to the quality of document clusters. Our results show that we are able to achieve a precision of 60% and a recall of 71% analyzed using manually annotated real-world data. Finally, we also make a comparative analysis of our work with the well-established rule-based system of MedISys and find that UPHED can be used in a cooperative way with MedISys to not only detect similar anomalies, but can also deliver more information about the specific outbreak of reported diseases.

Mining urban events from the tweet stream through a probabilistic mixture model

The geographical identification of content in Social Networks have enabled to bridge the gap between online social platforms and the physical world. Although vast amounts of data in such networks are due to breaking news or global occurrences, local events witnessed by users in situ are also present in these streams and of great importance for many city entities. Nowadays, unsupervised machine learning techniques, such as Tweet-SCAN, are able to retrospectively detect these local events from tweets. However, these approaches have limited abilities to reason about unseen observations in a principled way due to the lack of a proper probabilistic foundation. Probabilistic models have also been proposed for the task, but their event identification capabilities are far from those of Tweet-SCAN. In this paper, we identify two key factors which, when combined, boost the accuracy of such models. As a first key factor, we notice that the large amount of meaningless social data requires explicitly modeling non-event observations.Therefore, we propose to incorporate a background model that captures spatio-temporal fluctuations of non-event tweets. As a second key factor, we observe that the shortness of tweets hampers the application of traditional topic models. Thus, we integrate event detection and topic modeling, assigning topic proportions to events instead of assigning them to individual tweets. As a result, we propose Warble, a new probabilistic model and learning scheme for retrospective event detection that incorporates these two key factors. We evaluate Warble in a data set of tweets located in Barcelona during its festivities. The empirical results show that the model outperforms other state-of-the-art techniques in detecting various types of events while relying on a principled probabilistic framework that enables to reason under uncertainty.

Keyword extraction based on tf/idf for Chinese news document

Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.

A retrospective event detection method in news video

In this work we present a probabilistic learning approach to model video news story for retrospective event detection (RED). In this approach, both content and time information on a news video is utilized to transcribe the news story into terms, which are divided into classes by their semantics. Then a probabilistic model, composed of sub-models corresponding to the semantic classes respectively, is proposed. The model’s parameters are estimated by EM algorithm. Experiments showed that the proposed approach has better detection resolution.