Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)

What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Ital

HILT : High-Level Thesaurus Project. Phase IV and Embedding Project Extension : Final Report

Ensuring that Higher Education (HE) and Further Education (FE) users of the JISC IE can find appropriate learning, research and information resources by subject search and browse in an environment where most national and institutional service providers - usually for very good local reasons - use different subject schemes to describe their resources is a major challenge facing the JISC domain (and, indeed, other domains beyond JISC). Encouraging the use of standard terminologies in some services (institutional repositories, for example) is a related challenge. Under the auspices of the HILT project, JISC has been investigating mechanisms to assist the community with this problem through a JISC Shared Infrastructure Service that would help optimise the value obtained from expenditure on content and services by facilitating subject-search-based resource sharing to benefit users in the learning and research communities. The project has been through a number of phases, with work from earlier phases reported, both in published work elsewhere, and in project reports (see the project website: http://hilt.cdlr.strath.ac.uk/). HILT Phase IV had two elements - the core project, whose focus was 'to research, investigate and develop pilot solutions for problems pertaining to cross-searching multi-subject scheme information environments, as well as providing a variety of other terminological searching aids', and a short extension to encompass the pilot embedding of routines to interact with HILT M2M services in the user interfaces of various information services serving the JISC community. Both elements contributed to the developments summarised in this report

Adaptive learning program for developing employability skills

The paper aims to demonstrate the benefits of adaptive learning technologies as a viable alternative to time consuming tutor led individual support. It proposes to reveal how adaptive learning interventions can be effective in enriching student learning while targeting precise areas of development. This review will compile evidence on the nature and extent of Adaptive Learning tools used to develop employability skills among Higher Education institutions. This will be specifically for students undergoing studies at the graduate level. Given the short time available, a scoping study framework will be used to examine the scope of carrying out a full systematic review or identifying gaps in existing literature (Arksey and O’Malley, 2005). This design follows the general principles of a systematic review by following pre‐specified methods to reduce the risk of bias by selecting favourable studies, and extracting and analysing data that backs a particular hypothesis. That is, the methods are determined a priori, and are transparent and replicable

Predictive models for career progression

Linkedin est le plus grand réseau social pour les professionnels où les utilisateurs du service partagent toute leur histoire professionnelle. Dans ce travail, nous explorons les méthodes par lesquelles nous pouvons modéliser la trajectoire de carrière d'un candidat donné et prédire les changements de carrière futurs. La première partie de cette thèse est une tentative de normaliser les données sur les titres d'emploi, car nous avons constaté que la façon dont les utilisateurs de la plate-forme de réseautage social professionnel décident d'y saisir leurs titres varie énormément. Ensuite, nous explorons divers modèles prédictifs inspirés des modèles de langage de forme, ainsi que des modèles neuronaux séquentiels.LinkedIn is the largest social network for professionals where users of the service share all of their professional history. In this work we explore methods by which we can model the career trajectory of a given candidate and predict future career moves. The first part of this thesis is an attempt to normalize the job titles data as we have found that there is a great deal of variation in how the users of the professional social networking platform decide to input their titles. Then we move on to exploring various predictive models inspired form language models as well as sequential neuronal models

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Neural Based Statement Classification for Biased Language

Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.Comment: The Twelfth ACM International Conference on Web Search and Data Mining, February 11--15, 2019, Melbourne, VIC, Australi