Event extraction and representation: A case study for the portuguese language

Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents, objects, places, and time periods to be identified and represented. The extracted information can be represented by specialized ontologies, supporting knowledge-based reasoning and inference processes. In this work, we will describe, in detail, our proposal for event extraction from Portuguese documents. The proposed approach is based on a pipeline of specialized natural language processing tools; namely, a part-of-speech tagger, a named entities recognizer, a dependency parser, semantic role labeling, and a knowledge extraction module. The architecture is language-independent, but its modules are language-dependent and can be built using adequate AI (i.e., rule-based or machine learning) methodologies. The developed system was evaluated with a corpus of Portuguese texts and the obtained results are presented and analysed. The current limitations and future work are discussed in detail

Information Extraction for Event Ranking

Search engines are evolving towards richer and stronger semantic approaches, focusing on entity-oriented tasks where knowledge bases have become fundamental. In order to support semantic search, search engines are increasingly reliant on robust information extraction systems. In fact, most modern search engines are already highly dependent on a well-curated knowledge base. Nevertheless, they still lack the ability to effectively and automatically take advantage of multiple heterogeneous data sources. Central tasks include harnessing the information locked within textual content by linking mentioned entities to a knowledge base, or the integration of multiple knowledge bases to answer natural language questions. Combining text and knowledge bases is frequently used to improve search results, but it can also be used for the query-independent ranking of entities like events. In this work, we present a complete information extraction pipeline for the Portuguese language, covering all stages from data acquisition to knowledge base population. We also describe a practical application of the automatically extracted information, to support the ranking of upcoming events displayed in the landing page of an institutional search engine, where space is limited to only three relevant events. We manually annotate a dataset of news, covering event announcements from multiple faculties and organic units of the institution. We then use it to train and evaluate the named entity recognition module of the pipeline. We rank events by taking advantage of identified entities, as well as partOf relations, in order to compute an entity popularity score, as well as an entity click score based on implicit feedback from clicks from the institutional search engine. We then combine these two scores with the number of days to the event, obtaining a final ranking for the three most relevant upcoming events

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms

Report on the Second International Workshop on Narrative Extraction from Texts (Text2Story 2019)

The Second International Workshop on Narrative Extraction from Texts (Text2Story’19 [http://text2story19.inesctec.pt/]) was held on the 14th of April 2019, in conjunction with the 41st European Conference on Information Retrieval (ECIR 2019) in Cologne, Germany. The workshop provided a platform for researchers in IR, NLP, and design and visualization to come together and share the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited talks, ten research paper presentations, and a poster and demo session. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-2342/info:eu-repo/semantics/publishedVersio

Exploration of documents concerning Foundlings in Fafe along XIX Century

Dissertação de mestrado integrado em Informatics EngineeringThe abandonment of children and newborns is a problem in our society. In the last few decades, the introduction of contraceptive methods, the development of social programs and family planning were fundamental to control undesirable pregnancies and support families in need. But these developments were not enough to solve the abandonment epidemic. The anonymous abandonment has a dangerous aspect. In order to preserve the family identity, a child is usually left in a public place at night. Since children and newborns are one of the most vulnerable groups in our society, the time between the abandonment and the assistance of the child is potentially deadly. The establishment of public institutions in the past, such as the foundling wheel, was extremely important as a strategy to save lives. These institutions supported the abandoned children, while simultaneously providing a safer abandonment process, without compromising the anonymity of the family. The focus of the Master’s Project discussed in this dissertation is the analysis and processing of nineteenth century documents, concerning the Foundling Wheel of Fafe. The analysis of sample documents is the initial step in the development of an ontology. The ontology has a fundamental role in the organization and structure of the information contained in these historical documents. The identification of concepts and the relationships between them, culminates in a structured knowledge repository. Other important component is the development of a digital platform, where users are able to access the content stored in the knowledge repository and explore the digital archive, which incorporates the digitized version of documents and books from these historical institutions. The development of this project is important for some reasons. Directly, the implementation of a knowledge repository and a digital platform preserves information. These documents are mostly unique records and due to their age and advanced state of degradation, the substitution of the physical by digital access reduces the wear and tear associated to each consultation. Additionally, the digital archive facilitates the dissemination of valuable information. Research groups or the general public are able to use the platform as a tool to discover the past, by performing biographic, cultural or socio-economic studies over documents dated to the ninetieth century.O abandono de crianças e de recém-nascidos é um flagelo da sociedade. Nas últimas décadas, a introdução de métodos contraceptivos e de programas sociais foram essenciais para o desenvolvimento do planeamento familiar. Apesar destes avanços, estes programas não solucionaram a problemática do abandono de crianças e recém-nascidos. Problemas socioeconómicos são o principal factor que explica o abandono. O processo de abandono de crianças possui uma agravante perigosa. De forma a proteger a identidade da família, este processo ocorre normalmente em locais públicos e durante a noite. Como crianças e recém-nascidos constituem um dos grupos mais vulneráveis da sociedade, o tempo entre o abandono da criança e seu salvamento, pode ser demasiado longo e fatal. A casa da roda foi uma instituição introduzida de forma a tornar o processo de abandono anónimo mais seguro. O foco do Projeto de Mestrado discutido nesta dissertação é a análise e tratamento de documentos do século XIX, relativos à Casa da Roda de Fafe preservados pelo Arquivo Municipal de Fafe. A análise documental representa o ponto de partida do processo de desenvolvimento de uma ontologia. A ontologia possui um papel fundamental na organização e estruturação da informação contida nos documentos históricos. O processo de desenvolvimento de uma base de conhecimento consiste na identificação de conceitos e relações existentes nos documentos. Outra componente fundamental deste projecto é o desenvolvimento de uma plataforma digital, que permite utilizadores acederem à base de conhecimento desenvolvida. Os utilizadores podem pesquisar, explorar e adicionar informação à base de conhecimento. O desenvolvimento deste projecto possui importância. De forma imediata, a implementação de uma plataforma digital permite salvaguardar e preservar informação contida nos documentos. Estes documentos são os únicos registos existentes com esse conteúdo e muitos encontram-se num estado avançado de degradação. A substituição de acessos físicos por acessos digitais reduz o desgaste associado a cada consulta. O desenvolvimento da plataforma digital permite disseminar a informação contida na base documental. Investigadores ou o público em geral podem utilizar esta ferramenta com o intuito de realizar estudos biográficos, culturais e sociais sobre este arquivo histórico

Methods for improving entity linking and exploiting social media messages across crises

Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic

Processing temporal information in unstructured documents

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2013Temporal information processing has received substantial attention in the last few years, due to the appearance of evaluation challenges focused on the extraction of temporal information from texts written in natural language. This research area belongs to the broader field of information extraction, which aims to automatically find specific pieces of information in texts, producing structured representations of that information, which can then be easily used by other computer applications. It has the potential to be useful in several applications that deal with natural language, given that many languages, among which we find Portuguese, extensively refer to time. Despite that, temporal processing is still incipient for many language, Portuguese being one of them. The present dissertation has various goals. On one hand, it addresses this current gap, by developing and making available resources that support the development of tools for this task, employing this language, and also by developing precisely this kind of tools. On the other hand, its purpose is also to report on important results of the research on this area of temporal processing. This work shows how temporal processing requires and benefits from modeling different kinds of knowledge: grammatical knowledge, logical knowledge, knowledge about the world, etc. Additionally, both machine learning methods and rule-based approaches are explored and used in the development of hybrid systems that are capable of taking advantage of the strengths of each of these two types of approach.O processamento de informação temporal tem recebido bastante atenção nos últimos anos, devido ao surgimento de desafios de avaliação focados na extração de informação temporal de textos escritos em linguagem natural. Esta área de investigação enquadra-se no campo mais lato da extração de informação, que visa encontrar automaticamente informação específica presente em textos, produzindo representações estruturadas da mesma, que podem depois ser facilmente utilizadas por outras aplicações computacionais. Tem o potencial de ser útil em diversas aplicações que lidam com linguagem natural, dado o caráter quase ubíquo da referência ao tempo cronólogico em muitas línguas, entre as quais o Português. Apesar de tudo, o processamento temporal encontra-se ainda incipiente para bastantes línguas, sendo o Português uma delas. A presente dissertação tem vários objetivos. Por um lado vem colmatar esta lacuna existente, desenvolvendo e disponibilizando recursos que suportam o desenvolvimento de ferramentas para esta tarefa, utilizando esta língua, e desenvolvendo também precisamente este tipo de ferramentas. Por outro serve também para relatar resultados importantes da pesquisa nesta área do processamento temporal. Neste trabalho, mostra- -se como o processamento temporal requer e beneficia da modelação de conhecimento de diversos níveis: gramatical, lógico, acerca do mundo, etc. Adicionalmente, são explorados tanto métodos de aprendizagem automática como abordagens baseadas em regras, desenvolvendo-se sistemas híbridos capazes de tirar partido das vantagens de cada um destes dois tipos de abordagem.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/40140/2007