2,800 research outputs found
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
Predicting the Quality of Short Narratives from Social Media
An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc
The contribution of cause-effect link to representing the core of scientific paperâThe role of Semantic Link Network
The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
Inferring social relations from dialogues is vital for building emotionally
intelligent robots to interpret human language better and act accordingly. We
model the social network as an And-or Graph, named SocAoG, for the consistency
of relations among a group and leveraging attributes as inference cues.
Moreover, we formulate a sequential structure prediction task, and propose an
-- strategy to incrementally parse SocAoG for the
dynamic inference upon any incoming utterance: (i) an process
predicting attributes and relations conditioned on the semantics of dialogues,
(ii) a process updating the social relations based on related
attributes, and (iii) a process updating individual's attributes based
on interpersonal social relations. Empirical results on DialogRE and MovieGraph
show that our model infers social relations more accurately than the
state-of-the-art methods. Moreover, the ablation study shows the three
processes complement each other, and the case study demonstrates the dynamic
relational inference.Comment: Long paper (oral) accepted by ACL-IJCNLP 202
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
Screenplay Summarization Using Latent Narrative Structure
Most general-purpose extractive summarization models are trained on news
articles, which are short and present all important information upfront. As a
result, such models are biased on position and often perform a smart selection
of sentences from the beginning of the document. When summarizing long
narratives, which have complex structure and present information piecemeal,
simple position heuristics are not sufficient. In this paper, we propose to
explicitly incorporate the underlying structure of narratives into general
unsupervised and supervised extractive summarization models. We formalize
narrative structure in terms of key narrative events (turning points) and treat
it as latent in order to summarize screenplays (i.e., extract an optimal
sequence of scenes). Experimental results on the CSI corpus of TV screenplays,
which we augment with scene-level summarization labels, show that latent
turning points correlate with important aspects of a CSI episode and improve
summarization performance over general extractive algorithms leading to more
complete and diverse summaries.Comment: Accepted to appear at ACL 202
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Content-specific coordination of listeners' to speakers' EEG during communication
Cognitive neuroscience has recently begun to extend its focus from the isolated individual mind to two or more individuals coordinating with each other. In this study we uncover a coordination of neural activity between the ongoing electroencephalogram (EEG) of two peopleâa person speaking and a person listening. The EEG of one set of twelve participants (âspeakersâ) was recorded while they were narrating short stories. The EEG of another set of twelve participants (âlistenersâ) was recorded while watching audiovisual recordings of these stories. Specifically, listeners watched the superimposed videos of two speakers simultaneously and were instructed to attend either to one or the other speaker. This allowed us to isolate neural coordination due to processing the communicated content from the effects of sensory input. We find several neural signatures of communication: First, the EEG is more similar among listeners attending to the same speaker than among listeners attending to different speakers, indicating that listeners' EEG reflects content-specific information. Secondly, listeners' EEG activity correlates with the attended speakers' EEG, peaking at a time delay of about 12.5 s. This correlation takes place not only between homologous, but also between non-homologous brain areas in speakers and listeners. A semantic analysis of the stories suggests that listeners coordinate with speakers at the level of complex semantic representations, so-called âsituation modelsâ. With this study we link a coordination of neural activity between individuals directly to verbally communicated information
Esther Bick's legacy of infant observation at the Tavistock â Some reflections 60 years on.
This paper reviews the development of Infant Observation from its inception in 1948. It revisits Bick's original 1964 paper and explores current divergences from her original practice in the context of contemporary theories of psychoanalysis and adjacent disciplines and of relevant changes in society. It draws on the personal recollections of Bick's early students as well as the expanding published literature. It discusses seminar technique, the training of seminar leaders, and clinical and research applications of the observational method
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