Narrative Similarity as Common Summary

The ability to identify similarities between narratives has been argued to be central in human interactions. Previous work that sought to formalize this task has hypothesized that narrative similarity can be equated to the existence of a common summary between the narratives involved. We offer tangible psychological evidence in support of this hypothesis. Human participants in our empirical study were presented with triples of stories, and were asked to rate: (i) the degree of similarity between story A and story B; (ii) the appropriateness of story C as a summary of story A; (iii) the appropriateness of story C as a summary of story B. The story triples were selected systematically to span the space of their possible interrelations. Empirical evidence gathered from this study overwhelmingly supports the position that the higher the latter two ratings are, the higher the first rating also is. Thus, while this work does not purport to formally define either of the two tasks involved, it does argue that one can be meaningfully reduced to the other

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

The Discourse of Prayer in the Major Apocryphal Acts of the Apostles

This article presents a linguistic analysis of the prayers in the five major Apocryphal Acts of the Apostles. In the first part, the prayers are described in terms of form and structural elements. The second part offers a comparison with direct speech in the same works and Christian prayers and Greek prayers from other sources. The results suggest that this relatively uncharacterised discourse, closer to colloquial speech than any religious model, offers an authentic example of early Christian prayer

A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)

The alignment of heterogeneous sequential data (video to text) is an important and challenging problem. Standard techniques for this task, including Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from inherent drawbacks. Mainly, the Markov assumption implies that, given the immediate past, future alignment decisions are independent of further history. The separation between similarity computation and alignment decision also prevents end-to-end training. In this paper, we propose an end-to-end neural architecture where alignment actions are implemented as moving data between stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture supports a large variety of alignment tasks, including one-to-one, one-to-many, skipping unmatched elements, and (with extensions) non-monotonic alignment. Extensive experiments on semi-synthetic and real datasets show that our algorithm outperforms state-of-the-art baselines.Comment: Accepted at CVPR 2018 (Spotlight). arXiv file includes the paper and the supplemental materia

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision