There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.
The use of scripts in AI dates back to the 1970s @cite_20 @cite_13 ; in this conception, scripts were composed of complex events with no probabilistic semantics, which were difficult to learn automatically. In recent years, a growing body of research has investigated learning probabilistic co-occurrence models with simpler events. propose a model of co-occurrence of (verb, dependency) pairs, which can be used to infer such pairs from documents; give a superior model in the same general framework. give a method of generalizing from single sequences of pair events to collections of such sequences. apply a discriminative language model to the (verb, dependency) sequence modeling task, raising the question of to what extent event inference can be performed with standard language models applied to event sequences. describe a method of learning a co-occurrence based model of verbs with multiple coreference-based entity arguments.
@cite_13 @cite_20
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Scripts represent knowledge of stereotypical event sequences that can aid text understanding. Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument. We present a script learning approach that employs events with multiple arguments. Unlike previous work, we model the interactions between multiple entities in a script. Experiments on a large corpus using the task of inferring held-out events (the “narrative cloze evaluation”) demonstrate that modeling multi-argument events improves predictive accuracy. There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents. In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains. Inference has been a central topic in artificial intelligence from the start, but while automatic methods for formal deduction have advanced tremendously, comparatively little progress has been made on the problem of natural language inference (NLI), that is, determining whether a natural language hypothesis h can justifiably be inferred from a natural language premise p. The challenges of NLI are quite different from those encountered in formal deduction: the emphasis is on informal reasoning, lexical semantic knowledge, and variability of linguistic expression. This dissertation explores a range of approaches to NLI, beginning with methods which are robust but approximate, and proceeding to progressively more precise approaches. We first develop a baseline system based on overlap between bags of words. Despite its extreme simplicity, this model achieves surprisingly good results on a standard NLI evaluation, the PASCAL RTE Challenge. However, its effectiveness is limited by its failure to represent semantic structure. To remedy this lack, we next introduce the Stanford RTE system, which uses typed dependency trees as a proxy for semantic structure, and seeks a low-cost alignment between trees for p and h, using a cost model which incorporates both lexical and structural matching costs. This system is typical of a category of approaches to NLI based on approximate graph matching. We argue, however, that such methods work best when the entailment decision is based, not merely on the degree of alignment, but also on global features of the aligned 〈p, h〉 pair motivated by semantic theory. Seeking still greater precision, we devote the largest part of the dissertation to developing an approach to NLI based on a model of natural logic. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. Our system decomposes an inference problem into a sequence of atomic edits which transforms p into h; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. Finally, we address the problem of alignment for NLI, by developing a model of phrase-based alignment inspired by analogous work in machine translation, including an alignment scoring function, inference algorithms for finding good alignments, and training algorithms for choosing feature weights.