Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80 of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
where the arrow notation means ordered event pairs, i.e. event @math occurs before event @math . CP consists of two terms: the first is pair-wise mutual information (PMI) and the second is relative ordering of bigrams. PMI measures how often events occur as a pair (without considering their order); whereas relative ordering accounts for the order of the event pairs because temporal order is one of the strongest cues to causality @cite_23 @cite_33 @cite_34 . This work explicitly links their definitions to research using the Penn Discourse Treebank (PDTB) definition of contingency .
@cite_34 @cite_33 @cite_23
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Event pattern detection refers to identifying combinations of events matched to a user-specified query event pattern from a real-time event stream. Latency is an important measure of the performance of an event pattern detection system. Existing methods can be classified into the eager evaluation method and the lazy evaluation method depending on when each event arrival is evaluated. These methods have advantages and disadvantages in terms of latency depending on the event arrival rate. In this paper, we propose a hybrid eager-lazy evaluation method that combines the advantages of both methods. For each event type, the hybrid method, which we call APAM ( Adaptive Partitioning-And-Merging), determines which method to use: eager or lazy. We also propose a formal cost model to estimate the latency and propose a method of finding the optimal partition based on the cost model. Finally, we show through experiments that our method can improve the latency by up to 361.48 times over the eager evaluation method and 27.94 times over the lazy evaluation method using a synthetic data set. This paper is motivated by two applications, namely i) generalizations of cuckoo hashing, a computationally simple approach to assigning keys to objects, and ii) load balancing in content distribution networks, where one is interested in determining the impact of content replication on performance. These two problems admit a common abstraction: in both scenarios, performance is characterized by the maximum weight of a generalization of a matching in a bipartite graph, featuring node and edge capacities. Our main result is a law of large numbers characterizing the asymptotic maximum weight matching in the limit of large bipartite random graphs, when the graphs admit a local weak limit that is a tree. This result specializes to the two application scenarios, yielding new results in both contexts. In contrast with previous results, the key novelty is the ability to handle edge capacities with arbitrary integer values. An analysis of belief propagation algorithms (BP) with multivariate belief vectors underlies the proof. In particular, we show convergence of the corresponding BP by exploiting monotonicity of the belief vectors with respect to the so-called upshifted likelihood ratio stochastic order. This auxiliary result can be of independent interest, providing a new set of structural conditions which ensure convergence of BP. The efficient processing of large collections of patterns expressed as Boolean expressions over event streams plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. On the one hand, emerging user-centric applications, including computational advertising and selective information dissemination, demand determining and presenting to an end-user the relevant content as it is published. On the other hand, applications in real-time data analysis, including push-based multi-query optimization, computational finance and intrusion detection, demand meeting stringent subsecond processing requirements and providing high-frequency event processing. We achieve these event processing requirements by exploiting the shift towards multi-core architectures by proposing novel adaptive parallel compressed event matching algorithm (A-PCM) and online event stream re-ordering technique (OSR) that unleash an unprecedented degree of parallelism amenable for highly parallel event processing. In our comprehensive evaluation, we demonstrate the efficiency of our proposed techniques. We show that the adaptive parallel compressed event matching algorithm can sustain an event rate of up to 233,863 events second while state-of-the-art sequential event matching algorithms sustains only 36 events second when processing up to five million Boolean expressions. Contingency discourse relations play an important role in natural language understanding. In this paper we propose an unsupervised learning model to automatically identify contingency relationships between scenario-specific events in web news articles (on the Iraq war and on hurricane Katrina). The model generates ranked contingency relationships by identifying appropriate candidate event pairs for each scenario of a particular domain. Scenario-specific events, contributing towards the same objectives in a domain, are likely to be dependent on each other, and thus form good candidates for contingency relationships. In order to evaluate the ranked contingency relationships, we rely on the manipulation theory of causation and a comparison of precision-recall performance curves. We also perform various tests which bring insights into how people perceive causality. For example, our findings show that the larger the distance between two events, the more likely it becomes for the annotators to identify them as non-causal.