Equilíbrio Pontuado e a indústria de óleo e gás: uma análise da Comissão de Minas e Energia da Câmara dos Deputados

A indústria do petróleo e gás natural possui uma grande participação na economia nacional. Após a Constituição de 1988 houve um aumento significativo das participações governamentais, gerando mais recursos para os estados, que possuem representantes no Parlamento. A Comissão de Minas e Energia, da Câmara dos Deputados, é a principal comissão permanente que trata sobre políticas públicas relacionadas à Indústria de Óleo e Gás – IOG, no Brasil. Pouco se sabe sobre como se deu a participação dos parlamentares da principal comissão permanente na agenda desse setor. Com base na Teoria do Equilíbrio Pontuado, concluímos que dois importantes fatos alteraram a atuação parlamentar da CME sobre a IOG: a quebra do monopólio em 1995 e a descoberta do pré-sal em 2006

Khresmoi Professional: Multilingual Semantic Search for Medical Professionals

There is increasing interest in and need for innovative solutions to medical search. In this paper we present the EU funded Khresmoi medical search and access system, currently in year 3 of 4 of development across 12 partners . The Khresmoi system uses a component based architecture housed in the cloud to allow for the development of several innovative applications to support target users medical information needs. The Khresmoi search systems based on this architecture have been designed to support the multilingual and multimod al information needs of three target groups the general public, general practitioners and consultant radiologists. In this paper we focus on the presentation of the systems to support the latter two groups using semantic, multilingual text and image based (including 2D and 3D radiology images) search

Understandability and expertise in consumer health search : retrieving topically relevant and understandable health information on the Web

Search engines are concerned with retrieving relevant information to support a users information seeking task. In the health domain, access to understandable information is crucial as it has the potential to impact on peoples health decisions. In this thesis, we study two aspects that should be taken into account by modern health search engines: the user health expertise in the health domain and the document understandability. This thesis begins by considering the role of user expertise in the health domain. We investigate user search behavior through logfiles of several domain-specific health search engines. While most of the recent studies on health search behavior have been based on the search logs of commercial general purpose search engines, we performed here the important task of reproducing these studies on search logs of health search engines, finding out to what extent these results can be supported or not. Our query-log analysis can be used to understand health searchers better and even to predict the user expertise based on user behavior and their interactions with the search engine. Our investigation of document understandability in the health domain arises from the increasing concern that health documents on the Web are not suitable for health consumers. For that, we study the impact that preprocessing pipelines have on readability formulas, which are commonly used to estimate the understandability of documents. We also examined domain-specific methods to estimate the understandability of documents and how machine learning approaches can be employed to predict document understandability. In particular, for the health domain, documents should be considered more relevant if, apart from being topically relevant, they are also understandable by the searcher. For that, we need evaluation frameworks that consider other relevance dimensions beyond topicality. In this work, we propose a framework that delays the combination of scores for the different relevance dimensions, which facilitates the work of information retrieval practitioners by increasing the interpretability of the results. With such a framework, we evaluated various strategies to integrate understandability estimation into search engines, finding that learning-to-rank is the most effective approach. This work contributes to improving search engines tailored to consumer health search because it thoroughly investigates promises and pitfalls of understandability estimations and their integration into retrieval methods. As shown by our experiments, these methods would undoubtedly improve current health-focused search engines.16

How users search and what they search for in the medical domain ::understanding laypeople and experts through query logs

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America On- line (AOL), Health on the Net (HON), Turning Re- search Into Practice (TRIP) and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifer to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifer built, could be used to detect specifc user groups and then adapt search results to the user group

The influence of pre-processing on the estimation of readability of web documents

This paper investigates the effect that text pre-processing approaches have on the estimation of the readability of web pages. Readability has been highlighted as an important aspect of web search result personalisation in previous work. The most widely used text readability measures rely on surface level characteristics of text, such as the length of words and sentences. We demonstrate that different tools for extracting text from web pages lead to very different estimations of readability. This has an important implication for search engines because search result personalisation strategies that consider users reading ability may fail if incorrect text readability estimations are computed

Multilingual Detection of Personal Employment Status on Twitter

Detecting disclosures of individuals' employment status on social media can provide valuable information to match job seekers with suitable vacancies, offer social protection, or measure labor market flows. However, identifying such personal disclosures is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. Here, we examine three Active Learning (AL) strategies in real-world settings of extreme class imbalance, and identify five types of disclosures about individuals' employment status (e.g. job loss) in three languages using BERT-based classification models. Our findings show that, even under extreme imbalance settings, a small number of AL iterations is sufficient to obtain large and significant gains in precision, recall, and diversity of results compared to a supervised baseline with the same number of labels. We also find that no AL strategy consistently outperforms the rest. Qualitative analysis suggests that AL helps focus the attention mechanism of BERT on core terms and adjust the boundaries of semantic expansion, highlighting the importance of interpretable models to provide greater control and visibility into this dynamic learning process.Comment: ACL 2022 main conference. Data and models available at https://github.com/manueltonneau/twitter-unemploymen

Trectools: an open-source python library for information retrieval practitioners involved in TREC-like campaigns

This paper introduces TrecTools, a Python library for assisting Information Retrieval (IR) practitioners with TREC-like campaigns. IR practitioners tasked with activities like building test collections, evaluating systems, or analysing results from empirical experiments commonly have to resort to use a number of different software tools and scripts that each perform an individual functionality - and at times they even have to implement ad-hoc scripts of their own. TrecTools aims to provide a unified environment for performing these common activities. Written in the most popular programming language for Data Science, Python, TrecTools offers an object-oriented, easily extensible library. Existing systems, e.g., trec_eval, have considerable barrier to entry when it comes to modify or extend them. Furthermore, many existing IR measures and tools are implemented independently of each other, in different programming languages. TrecTools seeks to lower the barrier to entry and to unify existing tools, frameworks and activities into one common umbrella. Widespread adoption of a centralised solution for developing, evaluating, and analysing TREC-like campaigns will ease the burden on organisers and provide participants and users with a standard environment for common IR experimental activities. TrecTools is distributed as an open source library under the MIT license at https://github.com/joaopalotti/trectools

Retrieving information about medical symptoms

This paper details methods, results and analysis of the CLEF 2015 eHealth Evaluation Lab, Task 2. This task investigates the effectiveness of web search engines in providing access to medical information with the aim of fostering advances in the development of these technologie

CLEF 2017 eHealth evaluation lab overview

In this paper we provide an overview of the fifth edition of the CLEF eHealth evaluation lab. CLEF eHealth 2017 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring eHealth information in a multilingual setting. This year’s lab offered three tasks: Task 1 on multilingual information extraction to extend from last year’s task on French corpora, Task 2 on technologically assisted reviews in empirical medicine as a new pilot task, and Task 3 on patient-centered information retrieval (IR) building on the 2013-16 IR tasks. In total 32 teams took part in these tasks (11 in Task 1, 14 in Task 2, and 7 in Task 3). We also continued the replication track from 2016. Herein, we describe the resources created for these tasks, evaluation methodology adopted and provide a brief summary of participants of this year’s challenges and results obtained. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research and development