A Supervised Term-Weighting Method and its Application to Variable Extraction from Digital Media

Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables. This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the potential of the proposal as a first step for identifying different types of associations between words.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaXIX Simposio Argentino de Inteligencia ArtificialBuenos AiresArgentinaUniversidad de Palermo. Facultad de Ingeniería. Asociación Argentina de Inteligencia Artificial. Sociedad Argentina de Informátic

Causal graph extraction from news: a comparative study of time-series causality learning techniques

Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time series of the selected variables is given as input to a causal structure learning techniques to learn a causal graph associated with the topic that is being examined. The complete framework is applied to the New York Times dataset, which covers news for a period of 246 months (roughly 20 years), and is illustrated through a case study. An initial evaluation based on synthetic data is carried out to gain insight into the most effective time-series causality learning techniques. This evaluation comprises a systematic analysis of nine state-of-the-art causal structure learning techniques and two novel ensemble methods derived from the most effective techniques. Subsequently, the complete framework based on the most promising causal structure learning technique is evaluated with domain experts in a real-world scenario through the use of the presented case study. The proposed analysis offers valuable insights into the problems of identifying topic-relevant variables from large volumes of news and learning causal graphs from time seriesFil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Dalhousie University Halifax; CanadáFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Milios, Evangelos. Dalhousie University Halifax; CanadáFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Assessing Causality Structures learned from Digital Text Media

In this paper we describe a framework to uncover potential causal relations between event mentions from streaming text of news media. This framework relies on a dataset of manually labeled events to train a recurrent neural network for event detection. It then creates a time series of event clusters, where clusters are based on BERT contextual word embedding representations of the identified events. Using these time series dataset, we assess four methods based on Granger causality for inferring causal relations. Granger causality is a statistical concept of causality that is based on forecasting. It states that a cause occurs before the effect, and the cause produces unique changes in the effect, so past values of the cause help predict future values of the effect. The four analyzed methods are the pairwise Granger test, VAR(1), BigVar and SiMoNe. The framework is applied to the New York Times dataset, which covers news for a period of 246 months. This preliminary analysis delivers important insights into the nature of each method, identifies differences and commonalities, and points out some of their strengths and weaknesses.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Milios, Evangelos E.. Dalhousie University. Faculty of Computer Science; CanadáDocEng '20: ACM Symposium on Document Engineering 2020New YorkEstados UnidosAssociation for Computing Machiner

A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval

Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables.  This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of  retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the applicability of the proposed technique to address diverse problems such as building prediction models, supporting knowledge modeling, and achieving total recall

The Athena X-ray Integral Field Unit: a consolidated design for the system requirement review of the preliminary definition phase

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer, studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory, a versatile observatory designed to address the Hot and Energetic Universe science theme, selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), it aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over an hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR, browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters. Finally we briefly discuss on the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, and touch on communication and outreach activities, the consortium organisation, and finally on the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. (abridged).Comment: 48 pages, 29 figures, Accepted for publication in Experimental Astronomy with minor editin

The Athena X-ray Integral Field Unit: a consolidated design for the system requirement review of the preliminary definition phase

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory. Athena is a versatile observatory designed to address the Hot and Energetic Universe science theme, as selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), X-IFU aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over a hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR (i.e. in the course of its preliminary definition phase, so-called B1), browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters, such as the instrument efficiency, spectral resolution, energy scale knowledge, count rate capability, non X-ray background and target of opportunity efficiency. Finally, we briefly discuss the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, touch on communication and outreach activities, the consortium organisation and the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. The X-IFU will be provided by an international consortium led by France, The Netherlands and Italy, with ESA member state contributions from Belgium, Czech Republic, Finland, Germany, Poland, Spain, Switzerland, with additional contributions from the United States and Japan.The French contribution to X-IFU is funded by CNES, CNRS and CEA. This work has been also supported by ASI (Italian Space Agency) through the Contract 2019-27-HH.0, and by the ESA (European Space Agency) Core Technology Program (CTP) Contract No. 4000114932/15/NL/BW and the AREMBES - ESA CTP No.4000116655/16/NL/BW. This publication is part of grant RTI2018-096686-B-C21 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. This publication is part of grant RTI2018-096686-B-C21 and PID2020-115325GB-C31 funded by MCIN/AEI/10.13039/501100011033

Manually Labeled Data Set for the Ongoing Event Detection Task (2,200 news extracts from the NYT Annotated Corpus with manually labeled ongoing event triggers)

The present is a manually labeled data set for the task of Event Detection (ED). The task of ED consists of identifying event triggers, the word that most clearly indicates the occurrence of an event. The present data set consists of 2,200 news extracts from The New York Times (NYT) Annotated Corpus, separated into training (2,000) and testing (200) sets. Each news extract contains the plain text with the labels (event mentions), along with two metadata (publication date and an identifier). Labels description: We consider as event any ongoing real-world event or situation reported in the news articles. It is important to distinguish those events and situations that are in progress (or are reported as fresh events) at the moment the news is delivered from past events that are simply brought back, future events, hypothetical events, or events that will not take place. In our data set we only labeled as event the first type of event. Based on this criterion, some words that are typically considered as events are labeled as non-event triggers if they do not refer to ongoing events at the time the analyzed news is released. Take for instance the following news extract: "devaluation is not a realistic option to the current account deficit since it would only contribute to weakening the credibility of economic policies as it did during the last crisis." The only word that is labeled as event trigger in this example is "deficit" because it is the only ongoing event refereed in the news. Note that the words "devaluation", "weakening" and "crisis" could be labeled as event triggers in other news extracts, where the context of use of these words is different, but not in the given example. Further information: For a more detailed description of the data set and the data collection process please visit: https://cs.uns.edu.ar/~mmaisonnave/resources/ED_data. Data format: The dataset is split in two folders: training and testing. The first folder contains 2,000 XML files. The second folder contains 200 XML files. Each XML file has the following format. YYYYMMDDTHHMMSS ... ... ... The first three tags (pubdate, file-id and sent-idx) contain metadata information. The first one is the publication date of the news article that contained that text extract. The next two tags represent a unique identifier for the text extract. The file-id uniquely identifies a news article, that can hold several text extracts. The second one is the index that identifies that text extract inside the full article. The last tag (sentence) defines the beginning and end of the text extract. Inside that text are the tags. Each of these tags surrounds one word that was manually labeled as an event trigger.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Assessing the behavior and performance of a supervised term-weighting technique for topic-based retrieval

Topic-based retrieval is the task of seeking and retrieving material related to a topic of interest. This task involves two subtasks: selecting query terms and ranking the retrieved results. Supervised approaches to assess the importance of a term in a topic or class have demonstrated to be effective for guiding the query-term selection subtask. This article analyzes and evaluates FDD, a supervised term-weighting scheme that can be applied for query-term selection in topic-based retrieval. FDD weights terms based on two factors representing the descriptive and discriminating power of the terms with respect to the given topic. It then combines these two factor through the use of an adjustable parameter that allows to favor different aspects of retrieval, such as precision, recall or a balance between both. Previous preliminary studies have demonstrated the potential of FDD to identify useful query terms. However, preceding studies have limited the analysis to a single domain represented by a single data set with binary categories and have not compared FDD to other recently formulated term-weighting techniques. The contributions of this article are the following: (1) it presents an extensive analysis of the behavior of FDD as a function of its adjustable parameter; (2) it compares FDD against eighteen traditional and state-of-the-art weighting scheme; (3) it evaluates the performance of disjunctive queries built by combining terms selected using the analyzed methods; (4) it makes a full data set and the full code publicly available to replicate the reported analysis and foster future research in the area. The analysis and evaluations are performed on three data sets: two well-known text data sets, namely 20 Newsgroups and Reuters-21578, and the newly released data set. It is possible to conclude that despite its simplicity, FDD is competitive with state-of-the-art methods and has the important advantage of offering flexibility at the moment of adapting to specific task goals. The results also demonstrate that FDD offers a useful mechanism to explore different approaches to build complex queries.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin