One model, two languages: training bilingual parsers with harmonized treebanks

We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both. We test the approach on the Universal Dependency Treebanks, training with MaltParser and MaltOptimizer. The results show that these bilingual parsers are more than competitive, as most combinations not only preserve accuracy, but some even achieve significant improvements over the corresponding monolingual parsers. Preliminary experiments also show the approach to be promising on texts with code-switching and when more languages are added.Comment: 7 pages, 4 tables, 1 figur

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.Comment: 8 pages (11 including references), 2 figure

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

Social and Semantic Web Technologies for the Text-To-Knowledge Translation Process in Biomedicine

Currently, biomedical research critically depends on knowledge availability for flexible re-analysis and integrative post-processing. The voluminous biological data already stored in databases, put together with the abundant molecular data resulting from the rapid adoption of high-throughput techniques, have shown the potential to generate new biomedical discovery through integration with knowledge from the scientific literature. Reliable information extraction applications have been a long-sought goal of the biomedical text mining community. Both named entity recognition and conceptual analysis are needed in order to map the objects and concepts represented by natural language texts into a rigorous encoding, with direct links to online resources that explicitly expose those concepts semantics (see Figure 1).P08-TIC-4299 of J. ASevilla and TIN2009-13489 of DGICT, Madri

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web