Detecting events from web resources has attracted increasing research interests in recent years. Our focus in this paper is to detect events from photos on Flickr, an Internet image community website. The results can be used to facilitate user searching and browsing photos by events. The problem is challenging considering: (1) Flickr data is noisy, because there are photos unrelated to real-world events; (2) It is not easy to capture the content of photos. This paper presents our effort in detecting events from Flickr photos by exploiting the tags supplied by users to annotate photos. In particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet transform is employed to suppress noise. Then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. Afterwards, event-related tags are clustered such that each cluster, representing an event, consists of tags with similar temporal and locational distribution patterns as well as with similar associated photos. Finally, for each tag cluster, photos corresponding to the represented event are extracted. We evaluate the performance of our approach using a set of real data collected from Flickr. The experimental results demonstrate that our approach is effective in detecting events from the Flickr photo collection.

Events and stories can be characterized by a set of descriptive,

Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the semantic as well as temporal information in text streams is topic mining, which can further facilitate other knowledge discovery procedures. In many applications, we are facing multiple text streams which are related to each other and share common topics. The correlation among these streams can provide more meaningful and comprehensive clues for topic mining than those from each individual stream. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple streams, i.e. documents from different streams about the same topic may have different timestamps, which remains unsolved in the context of topic mining. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple streams based on the adjusted timestamps by the second step; the second step adjusts the timestamps of the documents according to the time distribution of the discovered topics by the first step. We perform these two steps alternately and a monotone convergence of our objective function is guaranteed. The effectiveness and advantage of our approach were justified by extensive empirical studies on two real data sets consisting of six research paper streams and two news article streams, respectively.

Abstract. We investigate the use of clustering methods for the task of grouping the text spans in a news article that refer to the same event. We provide evidence that the order in which events are described is structured in a way that can be exploited during clustering. We evaluate our approach on a corpus of news articles describing events that have occurred in the Iraqi War. 1

Abstract. In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users ’ interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user’s interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user's interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user's reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm based on the hierarchically structured profile model is also proposed. Experimental results indicate the advantages of our approach. 1

In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon-Grammar). Its role is to automatically feed the leaves with input data. 1.

Abstract—This paper presents the design of a system for feature extraction and classification of news articles from Croatian news sources. An overview of supervised and unsupervised text classification and clustering machine learning techniques is presented. The techniques described are those most widely used for text classification tasks. The paper discusses a number of issues particular to text classification of the news source material, from its collection and organization to particular problems related to the evaluation of method correctness and categorization efficiency on Croatian news documents. Uses of these techniques are discussed and a proposal for their quantitative evaluation over a newly developed testing news corpus is proposed. Index Terms—classification, clustering, Croatian news articles, machine learning, supervised learning, unsupervised learning

Abstract—This paper studies the problem of latent periodic topic analysis from timestamped documents. The examples of timestamped documents include news articles, sales records, financial reports, TV programs, and more recently, posts from social media websites such as Flickr, Twitter, and Facebook. Different from detecting periodic patterns in traditional time series database, we discover the topics of coherent semantics and periodic characteristics where a topic is represented by a distribution of words. We propose a model called LPTA (Latent Periodic Topic Analysis) that exploits the periodicity of the terms as well as term co-occurrences. To show the effectiveness of our model, we collect several representative datasets including Seminar, DBLP and Flickr. The results show that our model can discover the latent periodic topics effectively and leverage the information from both text and time well. Keywords-periodic topics; topic modeling; I.