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FABilT – finding answers in a billion triples
This submission presents the application of two coupled systems to the Billion Triples Challenge. The first system (Watson) provides the infrastructure which allows the second one (PowerAqua) to pose natural language queries to the billion triple datasets. Watson is a gateway to the Semantic Web: it crawls and indexes semantic data online to provide a variety of access mechanisms for human users and applications.We show here how we indexed most of the datasets provided for the challenge, thus obtaining an infrastructure (comprising web services, API, web interface, etc.) which supports the exploration of these datasets and makes them available to any Watson-based application. PowerAqua is an open domain question answering system which allows users to pose natural language queries to large scale collections of heterogeneous semantic data. In this paper, we discuss the issues we faced in configuring
PowerAqua and Watson for the challenge and report on our results. The system composed of Watson and PowerAqua, and applied to the Billion Triples Challenge, is called FABilT
Learning to predict closed questions on stack overflow
The paper deals with the problem of predicting whether the user’s question will be closed by the moderator on Stack Overflow, a popular question answering service devoted to software programming. The task along with data and evaluation metrics was offered as an open machine learning competition on Kaggle platform. To solve this problem, we employed a wide range of classification features related to users, their interactions, and post content. Classification was carried out using several machine learning methods. According to the results of the experiment, the most important features are characteristics of the user and topical features of the question. The best results were obtained using Vowpal Wabbit – an implementation of online learning based on stochastic gradient descent. Our results are among the best ones in overall ranking, although they were obtained after the official competition was over
Does consultation improve decision making?
This paper reports an experiment designed to test whether prior consultation within a group affects subsequent individual decision making in tasks where demonstrability of correct solutions is low. In our experiment subjects considered two paintings created by two different artists and were asked to guess which artist made each painting. We observed answers given by individuals under two treatments: in one, subjects were allowed the opportunity to consult with other participants before making their private decisions; in the other there was no such opportunity. Our primary findings are that subjects in the first treatment evaluate the opportunity to consult positively but they perform significantly worse and earn significantly less.Consultation; Decision making; Group decisions; Individual decisions
Towards an AI to Win Ghana's National Science and Maths Quiz
Can an AI win Ghana's National Science and Maths Quiz (NSMQ)? That is the
question we seek to answer in the NSMQ AI project, an open-source project that
is building AI to compete live in the NSMQ and win. The NSMQ is an annual live
science and mathematics competition for senior secondary school students in
Ghana in which 3 teams of 2 students compete by answering questions across
biology, chemistry, physics, and math in 5 rounds over 5 progressive stages
until a winning team is crowned for that year. The NSMQ is an exciting live
quiz competition with interesting technical challenges across speech-to-text,
text-to-speech, question-answering, and human-computer interaction. In this
ongoing work that began in January 2023, we give an overview of the project,
describe each of the teams, progress made thus far, and the next steps toward
our planned launch and debut of the AI in October for NSMQ 2023. An AI that
conquers this grand challenge can have real-world impact on education such as
enabling millions of students across Africa to have one-on-one learning support
from this AI.Comment: 7 pages. Under review at Deep Learning Indaba and Black in AI
Workshop @NeurIPS 202
Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies
This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game
Involving External Stakeholders in Project Courses
Problem: The involvement of external stakeholders in capstone projects and
project courses is desirable due to its potential positive effects on the
students. Capstone projects particularly profit from the inclusion of an
industrial partner to make the project relevant and help students acquire
professional skills. In addition, an increasing push towards education that is
aligned with industry and incorporates industrial partners can be observed.
However, the involvement of external stakeholders in teaching moments can
create friction and could, in the worst case, lead to frustration of all
involved parties. Contribution: We developed a model that allows analysing the
involvement of external stakeholders in university courses both in a
retrospective fashion, to gain insights from past course instances, and in a
constructive fashion, to plan the involvement of external stakeholders. Key
Concepts: The conceptual model and the accompanying guideline guide the
teachers in their analysis of stakeholder involvement. The model is comprised
of several activities (define, execute, and evaluate the collaboration). The
guideline provides questions that the teachers should answer for each of these
activities. In the constructive use, the model allows teachers to define an
action plan based on an analysis of potential stakeholders and the pedagogical
objectives. In the retrospective use, the model allows teachers to identify
issues that appeared during the project and their underlying causes. Drawing
from ideas of the reflective practitioner, the model contains an emphasis on
reflection and interpretation of the observations made by the teacher and other
groups involved in the courses. Key Lessons: Applying the model retrospectively
to a total of eight courses shows that it is possible to reveal hitherto
implicit risks and assumptions and to gain a better insight into the
interaction...Comment: Abstract shortened since arxiv.org limits length of abstracts. See
paper/pdf for full abstract. Paper is forthcoming, accepted August 2017.
Arxiv version 2 corrects misspelled author nam
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
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