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

Toward Automatic Role Identification in Unannotated Folk Tales

Abstract This paper presents an approach for automatically identifying high-level narrative structure information, particularly character roles, from unannotated folk tales. We introduce a new representation called action matrices to encode Propp's narrative theory on character role and their "sphere of action." We tested our approach in a fully automated system (Voz) using a corpus of 10 folk tales. Our experimental evaluation shows that action matrices capture useful information for role identification, provides insight into the error introduced by individual steps, and identifies the current bottlenecks

Identificação da valência emocional em sentenças de contos infantis

Orientador: Paula Dornhofer Paro CostaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A análise de sentimentos em textos tem sido amplamente explorada recentemente, principalmente usando técnicas de processamento de linguagem natural e aprendizado de máquina. No entanto, apesar dos avanços alcançados, ainda existem desafios significativos. Nosso trabalho explora a análise de sentimentos em textos narrativos, identificando as valências emocionais em sentenças pertencentes a contos infantis, que podem ser usadas, por exemplo, como recurso para aplicações destinadas a sintetizar narradores e atores virtuais no idioma português do Brasil. Usando técnicas de processamento de linguagem natural e um banco de dados afetivo chamado Anew-Br, criamos nosso algoritmo EMONT V1, que atribui valência emocional às frases do corpus desenvolvido. Foram adotadas duas abordagens diferentes para obter resultados comparáveis, aumentando assim a confiabilidade do nosso sistema: uma avaliação subjetiva que visa rotular frases por um grupo de 100 voluntários, que assumimos ser grund truth, e uma avaliação objetiva comparando os rótulos fornecidos por algumas plataformas comerciais que prometem fornecer funcionalidades semelhantes. Nosso algoritmo alcançou um desempenho de precisão equivalente a setores importantes de serviços de análise de sentimentos, como IBM Watson, API do Google Cloud Natural Language e Microsoft Azure Text Analytics. Os resultados dessa metodologia podem ser estendidos para outras frases infantis ou textos semelhantes, por exemplo, romance, história curta, crônica, fábula, parábola, anedota ou lendaAbstract: Sentiment analysis in texts has been widely explored recently, mainly using natural language processing and machine learning techniques. However, despite the advances achieved, there are still significant challenges. Our work explores the analysis of sentiments in narrative texts by identifying the emotional valences in sentences belonging to children's tales, which can be used, for example, as a resource for applications aimed at synthesizing narrators and virtual actors in the Brazilian Portuguese language. Using Natural Language Processing techniques and an affective database called Anew-Br, we created our EMONT V1 algorithm, which attributes emotional valence to the phrases of the developed corpus. Two different approaches were taken to obtain comparable results, thereby increasing the reliability of our system: a subjective assessment that aims to label sentences by a group of 100 volunteers, which we assume to be grund truth, and an objective assessment comparing the labels provided by some commercial platforms that promise to provide similar functionality. Our algorithm has achieved precision performance equivalent to significant industries of sentiment analysis services, such as IBM Watson, Google Cloud Natural Language API, and Microsoft Azure Text Analytics. The results of this methodology can be extended to other children sentences or similar texts, for instance, romance, short story, chronicle, fable, parable, anecdote, or legendMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica149147/2016-3CNP

Narrative Information Extraction with Non-Linear Natural Language Processing Pipelines

Computational narrative focuses on methods to algorithmically analyze, model, and generate narratives. Most current work in story generation, drama management or even literature analysis relies on manually authoring domain knowledge in some specific formal representation language, which is expensive to generate. In this dissertation we explore how to automatically extract narrative information from unannotated natural language text, how to evaluate the extraction process, how to improve the extraction process, and how to use the extracted information in story generation applications. As our application domain, we use Vladimir Propp's narrative theory and the corresponding Russian and Slavic folktales as our corpus. Our hypothesis is that incorporating narrative-level domain knowledge (i.e., Proppian theory) to core natural language processing (NLP) and information extraction can improve the performance of tasks (such as coreference resolution), and the extracted narrative information. We devised a non-linear information extraction pipeline framework which we implemented in Voz, our narrative information extraction system. Finally, we studied how to map the output of Voz to an intermediate computational narrative model and use it as input for an existing story generation system, thus further connecting existing work in NLP and computational narrative. As far as we know, it is the first end-to-end computational narrative system that can automatically process a corpus of unannotated natural language stories, extract explicit domain knowledge from them, and use it to generate new stories. Our user study results show that specific error introduced during the information extraction process can be mitigated downstream and have virtually no effect on the perceived quality of the generated stories compared to generating stories using handcrafted domain knowledge.Ph.D., Computer Science -- Drexel University, 201