Borsa Parole – A Market for Linguistic Speculation

This article describes a novel approach to linguistic field research consisting in exploiting the self-regulation of a market for collecting data on language use. The market is conceived as an output-agreement game with a purpose called Borsa Parole. The agreement can be traded with by the players what makes it adjustable. Borsa Parole has been conceived and is deployed for a linguistic study on the divergence of Italian dialects and vernaculars

Social Tagging: Exploring the Image, the Tags, and the Game

An increasing amount of images are being uploaded, shared, and retrieved on the Web. These large image collections need to be properly stored, organized and easily retrieved. Tags have a key role in image retrieval but it is difficult for those who upload the images to also undertake the quality tag assignment for potential future retrieval by others. Relying on professional keyword assignment is not a practical option for large image collections due to resource constraints. Although a number of content-based image retrieval systems have been launched, they have not demonstrated sufficient utility on large-scale image sources on the web, and are usually used as a supplement to existing text-based image retrieval systems. An alternative to professional image indexing can be social tagging -- with two major types being photo-sharing networks and image labeling games. Here we analyze these applications to evaluate their usefulness from the semantic point of view. We also investigate whether social tagging behaviour can be managed. The findings of the study have shown that social tagging can generate a sizeable number of tags that can be classified as interpretive for an image, and that tagging behaviour has a manageable and adjustable nature depending on tagging guidelines

Using Games to Create Language Resources: Successes and Limitations of the Approach

Abstract One of the more novel approaches to collaboratively creating language resources in recent years is to use online games to collect and validate data. The most significant challenges collaborative systems face are how to train users with the necessary expertise and how to encourage participation on a scale required to produce high quality data comparable with data produced by “traditional ” experts. In this chapter we provide a brief overview of collaborative creation and the different approaches that have been used to create language resources, before analysing games used for this purpose. We discuss some key issues in using a gaming approach, including task design, player motivation and data quality, and compare the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of using a gaming approach to resource creation and suggest key considerations for evaluating its utility in different research scenarios

Game with a Purposeによる映像エフェクト辞書構築手法の研究

指導教員：角　康

A game-based approach towards human augmented image annotation.

PhDImage annotation is a difficult task to achieve in an automated way. In this thesis, a human-augmented approach to tackle this problem is discussed and suitable strategies are derived to solve it. The proposed technique is inspired by human-based computation in what is called “human-augmented” processing to overcome limitations of fully automated technology for closing the semantic gap. The approach aims to exploit what millions of individual gamers are keen to do, i.e. enjoy computer games, while annotating media. In this thesis, the image annotation problem is tackled by a game based framework. This approach combines image processing and a game theoretic model to gather media annotations. Although the proposed model behaves similar to a single player game model, the underlying approach has been designed based on a two-player model which exploits the player’s contribution to the game and previously recorded players to improve annotations accuracy. In addition, the proposed framework is designed to predict the player’s intention through Markovian and Sequential Sampling inferences in order to detect cheating and improve annotation performances. Finally, the proposed techniques are comprehensively evaluated with three different image datasets and selected representative results are reported

Gamifying Language Resource Acquisition

PhD ThesisNatural Language Processing, is an important collection of methods for processing the vast amounts of available natural language text we continually produce. These methods make use of supervised learning, an approach that learns from large amounts of annotated data. As humans, we’re able to provide information about text that such systems can learn from. Historically, this was carried out by small groups of experts. However, this did not scale. This led to various crowdsourcing approaches being taken that used large pools of non-experts. The traditional form of crowdsourcing was to pay users small amounts of money to complete tasks. As time progressed, gamification approaches such as GWAPs, showed various benefits over the micro-payment methods used before. These included a cost saving, worker training opportunities, increased worker engagement and potential to far exceed the scale of crowdsourcing. While these were successful in domains such as image labelling, they struggled in the domain of text annotation, which wasn’t such a natural fit. Despite many challenges, there were also clearly many opportunities and benefits to applying this approach to text annotation. Many of these are demonstrated by Phrase Detectives. Based on lessons learned from Phrase Detectives and investigations into other GWAPs, in this work, we attempt to create full GWAPs for NLP, extracting the benefits of the methodology. This includes training, high quality output from non-experts and a truly game-like GWAP design that players are happy to play voluntarily

VideoTag: Encouraging the Effective Tagging of Internet Videos Through Tagging Games

A thesis submitted in partial fulfillment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyAbstract The tags and descriptions entered by video owners in video sharing sites are typically inadequate for retrieval purposes, yet the majority of video search still uses this text. This problem is escalating due to the ease with which users can self-publish videos, generating masses that are poorly labelled and poorly described. This thesis investigates how users tag videos and whether video tagging games can solve this problem by generating useful sets of tags. A preliminary study investigated tags in two social video sharing sites, YouTube and Viddler. YouTube contained many irrelevant tags because the system does not encourage users to tag their videos and does not promote tags as useful. In contrast, using tags as the sole means of categorisation in Viddler motivated users to enter a higher proportion of relevant tags. Poor tags were found in both systems, however, highlighting the need to improve video tagging. In order to give users incentives to tag videos, the VideoTag project in this thesis developed two tagging games, Golden Tag and Top Tag, and one non-game tagging system, Simply Tag, and conducted two experiments with them. In the first experiment VideoTag was a portal to play video tagging games whereas in the second experiment it was a portal to curate collections of special interest videos. Users preferred to tag videos using games, generating tags that were relevant to the videos and that covered a range of tag types that were descriptive of the video content at a predominately specific, objective level. Users were motivated by interest in the content rather than by game elements, and content had an effect on the tag types used. In each experiment, users predominately tagged videos using objective language, with a tendency to use specific rather than basic tags. There was a significant difference between the types of tags entered in the games and in Simply Tag, with more basic, objective vocabulary entered into the games and more specific, objective language entered into the non-game system. Subjective tags were rare but were more frequent in Simply Tag. Gameplay also had an influence on the types of tags entered; Top Tag generated more basic tags and Golden Tag generated more specific and subjective tags. Users were not attracted to use VideoTag by the games alone. Game mechanics had little impact on motivations to use the system. VideoTag used YouTube videos, but could not upload the tags to YouTube and so users could see no benefit for the tags they entered, reducing participation. Specific interest content was more of a motivator for use than games or tagging and that this warrants further research. In the current game-saturated climate, gamification of a video tagging system may therefore be most successful for collections of videos that already have a committed user base.University of Wolverhampto

Harnessing Collective Intelligence on Social Networks

Crowdsourcing is an approach to replace the work traditionally done by a single person with the collective action of a group of people via the Internet. It has established itself in the mainstream of research methodology in recent years using a variety of approaches to engage humans in solving problems that computers, as yet, cannot solve. Several common approaches to crowdsourcing have been successful, including peer production (in which the participants are inherently interested in contributing), microworking (in which participants are paid small amounts of money per task) and games or gamification (in which the participants are entertained as they complete the tasks). An alternative approach to crowdsourcing using social networks is proposed here. Social networks offer access to large user communities through integrated software applications and, as they mature, are utilised in different ways, with decentralised and unevenly-distributed organisation of content. This research investigates whether collective intelligence systems are facilitated better on social networks and how the contributed human effort can be optimised. These questions are investigated using two case studies of problem solving: anaphoric coreference in text documents and classifying images in the marine biology domain. Social networks themselves can be considered inherent, self-organised problem solving systems, an approach defined here as ?groupsourcing?, sharing common features with other crowdsourcing approaches; however, the benefits are tempered with the many challenges this approach presents. In comparison to other methods of crowdsourcing, harnessing collective intelligence on social networks offers a high-accuracy, data-driven and low-cost approach