Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Extracting the facts, namely entities and relations, from unstructured sources is an essential step in any knowledge base construction. At the same time, it is also necessary to ensure the completeness of the knowledge base by incrementally extracting the new facts from various sources. To date, the knowledge base completion is studied as a problem of knowledge refinement where the missing facts are inferred by reasoning about the information already present in the knowledge base. However, facts missed while extracting the information from multilingual sources are ignored. Hence, this work proposed a generic framework for knowledge base completion to enrich a knowledge base of crime-related facts extracted from online news articles in the English language, with the facts extracted from low resourced Indian language Hindi news articles. Using the framework, information from any low-resourced language news articles can be extracted without using language-specific tools like POS tags and using an appropriate machine translation tool. To achieve this, a clustering algorithm is proposed, which explores the redundancy among the bilingual collection of news articles by representing the clusters with knowledge base facts unlike the existing Bag of Words representation. From each cluster, the facts extracted from English language articles are bootstrapped to extract the facts from comparable Hindi language articles. This way of bootstrapping within the cluster helps to identify the sentences from a low-resourced language that are enriched with new information related to the facts extracted from a high-resourced language like English. The empirical result shows that the proposed clustering algorithm produced more accurate and high-quality clusters for monolingual and cross-lingual facts, respectively. Experiments also proved that the proposed framework achieves a high recall rate in extracting the new facts from Hindi news articles

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

Journalistic Knowledge Platforms: from Idea to Realisation

Journalistiske kunnskapsplattformer (JKPer) er en type intelligente informasjonssystemer designet for å forbedre nyhetsproduksjonsprosesser ved å kombinere stordata, kunstig intelligens (KI) og kunnskapsbaser for å støtte journalister. Til tross for sitt potensial for å revolusjonere journalistikkfeltet, har adopsjonen av JKPer vært treg, med forskere og store nyhetsutløp involvert i forskning og utvikling av JKPer. Den langsomme adopsjonen kan tilskrives den tekniske kompleksiteten til JKPer, som har ført til at nyhetsorganisasjoner stoler på flere uavhengige og oppgavespesifikke produksjonssystemer. Denne situasjonen kan øke ressurs- og koordineringsbehovet og kostnadene, samtidig som den utgjør en trussel om å miste kontrollen over data og havne i leverandørlåssituasjoner. De tekniske kompleksitetene forblir en stor hindring, ettersom det ikke finnes en allerede godt utformet systemarkitektur som ville lette realiseringen og integreringen av JKPer på en sammenhengende måte over tid. Denne doktoravhandlingen bidrar til teorien og praksisen rundt kunnskapsgrafbaserte JKPer ved å studere og designe en programvarearkitektur som referanse for å lette iverksettelsen av konkrete løsninger og adopsjonen av JKPer. Den første bidraget til denne doktoravhandlingen gir en grundig og forståelig analyse av ideen bak JKPer, fra deres opprinnelse til deres nåværende tilstand. Denne analysen gir den første studien noensinne av faktorene som har bidratt til den langsomme adopsjonen, inkludert kompleksiteten i deres sosiale og tekniske aspekter, og identifiserer de største utfordringene og fremtidige retninger for JKPer. Den andre bidraget presenterer programvarearkitekturen som referanse, som gir en generisk blåkopi for design og utvikling av konkrete JKPer. Den foreslåtte referansearkitekturen definerer også to nye typer komponenter ment for å opprettholde og videreutvikle KI-modeller og kunnskapsrepresentasjoner. Den tredje presenterer et eksempel på iverksettelse av programvarearkitekturen som referanse og beskriver en prosess for å forbedre effektiviteten til informasjonsekstraksjonspipelines. Denne rammen muliggjør en fleksibel, parallell og samtidig integrering av teknikker for naturlig språkbehandling og KI-verktøy. I tillegg diskuterer denne avhandlingen konsekvensene av de nyeste KI-fremgangene for JKPer og ulike etiske aspekter ved bruk av JKPer. Totalt sett gir denne PhD-avhandlingen en omfattende og grundig analyse av JKPer, fra teorien til designet av deres tekniske aspekter. Denne forskningen tar sikte på å lette vedtaket av JKPer og fremme forskning på dette feltet.Journalistic Knowledge Platforms (JKPs) are a type of intelligent information systems designed to augment news creation processes by combining big data, artificial intelligence (AI) and knowledge bases to support journalists. Despite their potential to revolutionise the field of journalism, the adoption of JKPs has been slow, with scholars and large news outlets involved in the research and development of JKPs. The slow adoption can be attributed to the technical complexity of JKPs that led news organisation to rely on multiple independent and task-specific production system. This situation can increase the resource and coordination footprint and costs, at the same time it poses a threat to lose control over data and face vendor lock-in scenarios. The technical complexities remain a major obstacle as there is no existing well-designed system architecture that would facilitate the realisation and integration of JKPs in a coherent manner over time. This PhD Thesis contributes to the theory and practice on knowledge-graph based JKPs by studying and designing a software reference architecture to facilitate the instantiation of concrete solutions and the adoption of JKPs. The first contribution of this PhD Thesis provides a thorough and comprehensible analysis of the idea of JKPs, from their origins to their current state. This analysis provides the first-ever study of the factors that have contributed to the slow adoption, including the complexity of their social and technical aspects, and identifies the major challenges and future directions of JKPs. The second contribution presents the software reference architecture that provides a generic blueprint for designing and developing concrete JKPs. The proposed reference architecture also defines two novel types of components intended to maintain and evolve AI models and knowledge representations. The third presents an instantiation example of the software reference architecture and details a process for improving the efficiency of information extraction pipelines. This framework facilitates a flexible, parallel and concurrent integration of natural language processing techniques and AI tools. Additionally, this Thesis discusses the implications of the recent AI advances on JKPs and diverse ethical aspects of using JKPs. Overall, this PhD Thesis provides a comprehensive and in-depth analysis of JKPs, from the theory to the design of their technical aspects. This research aims to facilitate the adoption of JKPs and advance research in this field.Doktorgradsavhandlin

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

Where are you talking about? Advances and Challenges of Geographic Analysis of Text with Application to Disease Monitoring

The Natural Language Processing task we focus on in this thesis is Geoparsing. Geoparsing is the process of extraction and grounding of toponyms (place names). Consider this sentence: "The victims of the Spanish earthquake off the coast of Malaga were of American and Mexican origin." Four toponyms will be extracted (called Geotagging) and grounded to their geographic coordinates (called Toponym Resolution). However, our research goes further than any previous work by showing how to distinguish the literal place(s) of the event (Spain, Malaga) from other linguistic types/uses such as nationalities (Mexican, American), improving downstream task accuracy. We consolidate and extend the Standard Evaluation Framework, discuss key research problems, then present concrete solutions in order to advance each stage of geoparsing. For geotagging, as well as training a SOTA neural Location-NER tagger, we simplify Metonymy Resolution with a novel minimalist feature extraction combined with an LSTM-based classifier, matching SOTA results. For toponym resolution, we deploy the latest deep learning methods to achieve SOTA performance by augmenting neural models with hitherto unused geographic features called Map Vectors. With each research project, we provide high-quality datasets and system prototypes, further building resources in this field. We then show how these geoparsing advances coupled with our proposed Intra-Document Analysis can be used to associate news articles with locations in order to monitor the spread of public health threats. To this end, we evaluate our research contributions with production data from a real-time downstream application to improve geolocation of news events for disease monitoring. The data was made available to us by the Joint Research Centre (JRC), which operates one such system called MediSys that processes incoming news articles in order to monitor threats to public health and make these available to a variety of governmental, business and non-profit organisations. We also discuss steps towards an end-to-end, automated news monitoring system and make actionable recommendations for future work. In summary, the thesis aims are twofold: (1) Generate original geoparsing research aimed at advancing each stage of the pipeline by addressing pertinent challenges with concrete solutions and actionable proposals. (2) Demonstrate how this research can be applied to news event monitoring to increase the efficacy of existing biosurveillance systems, e.g. European Commission’s MediSys.I was generously funded by DREAM CDT, which was funded by NERC of UKRI

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories