Automating the anonymisation of textual corpora

[EU] Gaur egun, testu berriak etengabe sortzen doaz sare sozialetako mezu, osasun-txosten, dokumentu o zial eta halakoen ondorioz. Hala ere, testuok informazio pertsonala baldin badute, ezin dira ikerkuntzarako edota beste helburutarako baliatu, baldin eta aldez aurretik ez badira anonimizatzen. Anonimizatze hori automatikoki egitea erronka handia da eta askotan hutsetik anotatutako domeinukako datuak behar dira, ez baita arrunta helburutzat ditugun testuinguruetarako anotatutako corpusak izatea. Hala, tesi honek bi helburu ditu: (i) Gaztelaniazko elkarrizketa espontaneoz osatutako corpus anonimizatu berri bat konpilatu eta eskura jartzea, eta (ii) sortutako baliabide hau ustiatzea informazio sentiberaren identi kazio-teknikak aztertzeko, helburu gisa dugun domeinuan testu etiketaturik izan gabe. Hala, lehenengo helburuari lotuta, ES-Port izeneko corpusa sortu dugu. Telekomunikazio-ekoizle batek ahoz laguntza teknikoa ematen duenean sortu diren 1170 elkarrizketa espontaneoek osatzen dute corpusa. Ordezkatze-tekniken bidez anonimizatu da, eta ondorioz emaitza testu irakurgarri eta naturala izan da. Hamaika anonimizazio-kategoria landu dira, eta baita hizkuntzakoak eta hizkuntzatik kanpokoak diren beste zenbait anonimizazio-fenomeno ere, hala nola, kode-aldaketa, barrea, errepikapena, ahoskatze okerrak, eta abar. Bigarren helburuari lotuta, berriz, anonimizatu beharreko informazio sentibera identi katzeko, gordailuan oinarritutako Ikasketa Aktiboa erabili da, honek helburutzat baitu ahalik eta testu anotatu gutxienarekin sailkatzaile ahalik eta onena lortzea. Horretaz gain, emaitzak hobetzeko, eta abiapuntuko hautaketarako eta galderen hautaketarako estrategiak aztertzeko, Ezagutza Transferentzian oinarritutako teknikak ustiatu dira, aldez aurretik anotatuta zegoen corpus txiki bat oinarri hartuta. Emaitzek adierazi dute, lan honetan aukeratutako metodoak egokienak izan direla abiapuntuko hautaketa egiteko eta kontsulta-estrategia gisa iturri eta helburu sailkapenen zalantzak konbinatzeak Ikasketa Aktiboa hobetzen duela, ikaskuntza-kurba bizkorragoak eta sailkapen-errendimendu handiagoak lortuz iterazio gutxiagotan.[EN] A huge amount of new textual data are created day by day through social media posts, health records, official documents, and so on. However, if such resources contain personal data, they cannot be shared for research or other purposes without undergoing proper anonymisation. Automating such task is challenging and often requires labelling in-domain data from scratch since anonymised annotated corpora for the target scenarios are rarely available. This thesis has two main objectives: (i) to compile and provide a new corpus in Spanish with annotated anonymised spontaneous dialogue data, and (ii) to exploit the newly provided resource to investigate techniques for automating the sensitive data identification task, in a setting where initially no annotated data from the target domain are available. Following such aims, first, the ES-Port corpus is presented. It is a compilation of 1170 spontaneous spoken human-human dialogues from calls to the technical support service of a telecommunications provider. The corpus has been anonymised using the substitution technique, which implies the result is a readable natural text, and it contains annotations of eleven different anonymisation categories, as well as some linguistic and extra-linguistic phenomena annotations like code-switching, laughter, repetitions, mispronunciations, and so on. Next, the compiled corpus is used to investigate automatic sensitive data identification within a pool-based Active Learning framework, whose aim is to obtain the best possible classifier having to annotate as little data as possible. In order to improve such setting, Knowledge Transfer techniques from another small available anonymisation annotated corpus are explored for seed selection and query selection strategies. Results show that using the proposed seed selection methods obtain the best seeds on which to initialise the base learner's training and that combining source and target classifiers' uncertainties as query strategy improves the Active Learning process, deriving in steeper learning curves and reaching top classifier performance in fewer iterations

Opinion mining using combinational approach for different domains

An increase in use of web produces large content of information about products. Online reviews are used to make decision by peoples. Opinion mining is vast research area in which different types of reviews are analyzed. Several issues are existing in this area. Domain adaptation is emerging issue in opinion mining. Labling of data for every domain is time consuming and costly task. Hence the need arises for model that train one domain and applied it on other domain reducing cost aswell as time. This is called domain adaptation which is addressed in this paper. Using maximum entropy and clustering technique source domains data is trained. Trained data from source domain is applied on target data to labeling purpose A result shows moderate accuracy for 5 fold cross validation and combination of source domains for Blitzer et al (2007) multi domain product dataset

Semantic and pragmatic characterization of learning objects

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Identifying Graphs from Noisy Observational Data

There is a growing amount of data describing networks -- examples include social networks, communication networks, and biological networks. As the amount of available data increases, so does our interest in analyzing the properties and characteristics of these networks. However, in most cases the data is noisy, incomplete, and the result of passively acquired observational data; naively analyzing these networks without taking these errors into account can result in inaccurate and misleading conclusions. In my dissertation, I study the tasks of entity resolution, link prediction, and collective classification to address these deficiencies. I describe these tasks in detail and discuss my own work on each of these tasks. For entity resolution, I develop a method for resolving the identities of name mentions in email communications. For link prediction, I develop a method for inferring subordinate-manager relationships between individuals in an email communication network. For collective classification, I propose an adaptive active surveying method to address node labeling in a query-driven setting on network data. In many real-world settings, however, these deficiencies are not found in isolation and all need to be addressed to infer the desired complete and accurate network. Furthermore, because of the dependencies typically found in these tasks, the tasks are inherently inter-related and must be performed jointly. I define the general problem of graph identification which simultaneously performs these tasks; removing the noise and missing values in the observed input network and inferring the complete and accurate output network. I present a novel approach to graph identification using a collection of Coupled Collective Classifiers, C3, which, in addition to capturing the variety of features typically used for each task, can capture the intra- and inter-dependencies required to correctly infer nodes, edges, and labels in the output network. I discuss variants of C3 using different learning and inference paradigms and show the superior performance of C3, in terms of both prediction quality and runtime performance, over various previous approaches. I then conclude by presenting the Graph Alignment, Identification, and Analysis (GAIA) open-source software library which not only provides an implementation of C3 but also algorithms for various tasks in network data such as entity resolution, link prediction, collective classification, clustering, active learning, data generation, and analysis

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research