Titulación automática de preguntas en encuestas electorales

This paper describes the work carried out for automatically generating titles for questions included in the opinion polls contained in CIS databases (Centro de Investigaciones Sociológicas – Spanish Center of Sociological Research). In the context of CIS, the title of a question should meet two requirements: from the point of view of form, it has to be grammatically correct and similar in style to existing ones; from the point of view of content, it must contain the subject of the question and the different options for answering. These conditions for form and content of titles discourage the use of techniques used in similar problems, such as automatic abstracting or machine learning with a training corpus, but rather favor a methodology based on an analysis and knowledge of the domain. To illustrate the analysis and the resolution strategy of the problem, we have selected a set of questions related to elections, due to their strategic importance and to CIS’s own specialization in opinion polls. The process followed and the subsequent evaluation of results are discussed in detail, with an assessment of both qualitative and quantitative aspects. The evaluation shows that 88.73% of the generated titles are in strict accordance with CIS’s requisites on form and content, resulting in reduced time spent by the institution’s qualified personnel on manual work.Este artículo describe el trabajo realizado para la generación automática de los títulos de las preguntas pertenecientes a las encuestas de opinión que existen en las bases de datos del CIS (Centro de Investigaciones Sociológicas). Dentro del contexto del CIS, el título de una pregunta debe cumplir dos requisitos: desde el punto de vista de la forma, debe ser gramaticalmente correcto y tener un estilo similar a los ya existentes; y, desde el punto de vista del contenido, debe albergar el tema de la pregunta y las distintas categorías de respuesta. Estas restricciones en cuanto a la forma y al contenido de los títulos desaconsejan el uso de técnicas empleadas en problemas similares, como el resumen automático o aprendizaje automático con corpus de entrenamiento, a favor de una metodología basada en el análisis y conocimiento del dominio. Para ilustrar el análisis y la estrategia de resolución del problema seguidos, hemos seleccionado las preguntas relacionadas con temas electorales, debido a la importancia estratégica y a la especialización del CIS en este tipo de encuestas. Se describe en detalle el procedimiento seguido y la evaluación de los resultados, valorando tanto los aspectos cualitativos como los cuantitativos. La evaluación muestra que el 88,73% de los títulos generados cumplen estrictamente con los requisitos de forma y contenido impuestos por el CIS, lo que supone un ahorro en el trabajo manual del personal cualificado de la institución

Continuous Health Interface Event Retrieval

Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources to retrieve complex biological events, such as cardiovascular volume overload. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on relevant biological systems. They are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using an event operator language.Comment: ACM International Conference on Multimedia Retrieval 2020 (ICMR 2020), held in Dublin, Ireland from June 8-11, 202

Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from elite politicians in the U.S., we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.Comment: 16 pages, 5 figures. To be published in EPJ Data Scienc

Rewarding the Location of Terms in Sentences to Enhance Probabilistic Information Retrieval

In most traditional retrieval models, the weight (or probability) of a query term is estimated based on its own distribution or statistics. Intuitively, however, the nouns are more important in information retrieval and are more often found near the beginning and the end of sentences. In this thesis, we investigate the effect of rewarding the terms based on their location in sentences on information retrieval. Particularly, we propose a kernel-based method to capture the term placement pattern, in which a novel Term Location retrieval model is derived in combination with the BM25 model to enhance probabilistic information retrieval. Experiments on five TREC datasets of varied size and content indicates that the proposed model significantly outperforms the optimized BM25 and DirichletLM in MAP over all datasets with all kernel functions, and excels compared to the optimized BM25 and DirichletLM over most of the datasets in P@5 and P@20 with different kernel functions

Automated Detection of Financial Events in News Text

Today’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task. Therefore, the studies composing this dissertation investigate 1) how to accurately identify financial events in news text, and 2) how to effectively use such extracted events in financial applications. Based on a detailed evaluation of current event extraction systems, this thesis presents a competitive, knowledge-driven, semi-automatic system for financial event extraction from text. A novel pattern language, which makes clever use of the system’s underlying knowledge base, allows for the definition of simple, yet expressive event extraction rules that can be applied to natural language texts. The system’s knowledge-driven internals remain synchronized with the latest market developments through the accompanying event-triggered update language for knowledge bases, enabling the definition of update rules. Additional research covered by this dissertation investigates the practical applicability of extracted events. In automated stock trading experiments, the best performing trading rules do not only make use of traditional numerical signals, but also employ news-based event signals. Moreover, when cleaning stock data from disruptions caused by financial events, financial risk analyses yield more accurate results. These results suggest that events detected in news can be used advantageously as supplementary parameters in financial applications