Time associated with news events has been leveraged as a complementary dimension to text in several applications such as temporal information retrieval, news event linking, etc. Short textual event descriptions (e.g., single sentences) are prevalent in web documents (also considered as inputs in the above applications) and often lack explicit temporal expressions for grounding them to a precise time period. For example, the event description, "France swears in Emmanuel Macron as the 25th President", lacks temporal cues to indicate that the event occurred in the year "2017". Thus, we address the problem of estimating event focus time defined as a time interval with maximum association thereby indicating its occurrence period. We propose several estimators that leverage distributional event and time representations learned from large external document collections by adapting the word2vec paradigm. Extensive experiments using two real-world datasets and 100 Wikipedia events show that our method outperforms several state-of-the-art baselines.

We demonstrate BioNex, a system to mine, rank and visualize biomedical news events. BioNex takes biomedical queries such as "Ebola virus disease" and retrieves the k most relevant news events for them. To achieve this we first mine the generic news events by clustering them on a daily basis using general named entities and textual features. These clusters are also tagged with disambiguated biomedical entities which aid in biomedical news event exploration. The clusters are then used to compute the importance scores for the event clusters based on a combination of textual, semantic, popularity and historical importance features. BioNex also visualizes the retrieved event clusters to highlight the top news events and corresponding news articles for the given query. The visualization also provides the context for news events using (1) a chain of historically relevant news event clusters, and (2) other non-biomedical events from the same day.

We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.

It is often difficult to ground text to precise time intervals due to the inherent uncertainty arising from either missing or multiple expressions at year, month, and day time granularities. We address the problem of estimating an excerpt-time model capturing the temporal scope of a given news article excerpt as a probability distribution over chronons. For this, we propose a semi-supervised distribution propagation framework that leverages redundancy in the data to improve the quality of estimated time models. Our method generates an event graph with excerpts as nodes and models various inter-excerpt relations as edges. It then propagates empirical excerpt-time models estimated for temporally annotated excerpts, to those that are strongly related but miss annotations. In our experiments, we first generate a test query set by randomly sampling 100 Wikipedia events as queries. For each query, making use of a standard text retrieval model, we then obtain top-10 documents with an average of 150 excerpts. From these, each temporally annotated excerpt is considered as gold standard. The evaluation measures are first computed for each gold standard excerpt for a single query, by comparing the estimated model with our method to the empirical model from the original expressions. Final scores are reported by averaging over all the test queries. Experiments on the English Gigaword corpus show that our method estimates significantly better time models than several baselines taken from the literature.

For a general user, easy access to vast amounts of online information available on past events has made retrospection much harder. We propose a problem of automatic event digest generation to aid effective and efficient retrospection. For this, in addition to text, a digest should maximize the reportage of time, geolocations, and entities to present a holistic view on the past event of interest.