Empirical Methodology for Crowdsourcing Ground Truth

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa

Crowdsourcing Emotions in Music Domain

An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

Crowdsourcing with Semantic Differentials: A Game to Investigate the Meaning of Form

This paper presents a tool to collect empirical data about the collaborative meaning of form. We developed an online crowdscouring game, in which two users rate randomly assigned three-dimensional shapes. The more similar the ratings are, the more points both players get. This crowdsourcing method allows identifying what certain shapes mean to people. This paper is a contribution on two levels: First, the game presents a particular research method—an experimental survey using semantic differentials—, which adds a motivational benefit for the participants: It is fun to play. Also, it involves a quality control mechanism through the pairing of two participants who rate the same image and therefore act as verification. Second, the semantic collection of forms might help designers to better control the connotative meanings embedded in their designs. This paper is focused on introducing the game; the analysis of the data will be covered in further research

Ten years of MIREX: reflections, challenges and opportunities

The Music Information Retrieval Evaluation eXchange (MIREX) has been run annually since 2005, with the October 2014 plenary marking its tenth iteration. By 2013, MIREX has evaluated approximately 2000 individual music information retrieval (MIR) algorithms for a wide range of tasks over 37 different test collections. MIREX has involved researchers from over 29 different contrives with a median of 109 individual participants per year. This pater summarizes the history of MIREX form its earliest planning meeting in 2001 to the present. It reflects upon the administrative, financial, and technological challenges MIREX has faced and describes how those challenges have been surmounted. We propose new funding models, a distributed evaluation framework, and more holistic user experience evaluation tasks-some evolutionary, some revolutionary-for the continued success of MIREX. We hope that this paper will inspire MIR community members to contribute their ideas so MIREX can have many more successful years to come

Design problems in crowdsourcing: improving the quality of crowd-based data collection

Text, images, and other types of information objects can be described in many ways. Having detailed metadata and various people's interpretations of the object helps in providing better access and use. While collecting novel descriptions is challenging, crowdsourcing is presenting new opportunities to do so. Large-scale human contributions open the door to latent information, subjective judgments, and other encoding of data that is otherwise difficult to infer algorithmically. However, such contributions are also subject to variance from the inconsistencies of human interpretation. This dissertation studies the problem of variance in crowdsourcing and investigates how it can be controlled both through post-collection modeling and better collection-time design decisions. Crowd-contributed data is affected by many inconsistencies that differ from automated processes: differences in attention, interpretation, skill, and engagement. The types of tasks that we require of humans are also more inherently abstract and more difficult to agree on. Particularly, qualitative or judgment-based tasks may be subjective, affected by contributor opinions and tastes. Approaches to understanding contribution variance and improve data quality are studied in three spaces. First, post-collection modeling is pursued as a way of improving crowdsourced data quality, looking at whether factors including time, experience, and agreement with others provide indicators of contributions quality. Secondly, collection-time design problems are studied, comparing design manipulations for a controlled set of tasks. Since crowdsourcing is borne out of an interaction, not all crowdsourcing data corrections are posterior: it also matters how you collect that data. Finally, designing for subjective contexts is studied. Crowds are well-positioned to teach us about how information can be adapted to different person-specific needs, but treating subjective tasks similarly to other tasks results in unnecessary error. The primary contribution of this work is an understanding of crowd data quality improvements from non-adversarial perspectives: that is, focusing on sources of variance or errors beyond poor contributors. This includes findings that: 1. Collection interface design has a vital influence on the quality of collected data, and better guiding contributors can improve crowdsourced contribution quality without greatly raising the cost of collection nor impeding other quality control strategies. 2. Different interpretations of instructions threaten reliability and accuracy in crowdsourcing. This source of problems even affects trustworthy, attentive contributors. However, contributor quality can be inferred very early in an interaction for possible interventions. 3. Certain design choices improve the quality of contributions in tasks that call for them. Anchoring reduces contributor-specific error, training affirms or corrects contributors' understanding of the task, and performance feedback can motivate middling contributors to exercise more care. Particularly notable due to its simplicity, an intervention that forefronts instructions behind an explicitly dismissable window improves contribution quality greatly. 4. Paid crowdsourcing, often used for tasks with an assumed ground truth, can be also be applied in subjective contexts. It is promising for on-demand personalization contexts, such as recommendation without prior data for training. 5. Two approaches are found to improve the quality of tasks for subjective crowdsourcing. Matching contributors to a target person based on similarity is good for long-term interactions or for bootstrapping multi-target systems. Alternately, explicitly asking contributors to make sense of a target person and customize work for them is especially good for tasks with broad decision spaces and is more enjoyable to perform. The findings in this dissertation contribute to the crowdsourcing research space as well as providing practical improvements to crowd collection best practices

Augmenting the performance of image similarity search through crowdsourcing

Crowdsourcing is defined as “outsourcing a task that is traditionally performed by an employee to a large group of people in the form of an open call” (Howe 2006). Many platforms designed to perform several types of crowdsourcing and studies have shown that results produced by crowds in crowdsourcing platforms are generally accurate and reliable. Crowdsourcing can provide a fast and efficient way to use the power of human computation to solve problems that are difficult for machines to perform. From several different microtasking crowdsourcing platforms available, we decided to perform our study using Amazon Mechanical Turk. In the context of our research we studied the effect of user interface design and its corresponding cognitive load on the performance of crowd-produced results. Our results highlighted the importance of a well-designed user interface on crowdsourcing performance. Using crowdsourcing platforms such as Amazon Mechanical Turk, we can utilize humans to solve problems that are difficult for computers, such as image similarity search. However, in tasks like image similarity search, it is more efficient to design a hybrid human–machine system. In the context of our research, we studied the effect of involving the crowd on the performance of an image similarity search system and proposed a hybrid human–machine image similarity search system. Our proposed system uses machine power to perform heavy computations and to search for similar images within the image dataset and uses crowdsourcing to refine results. We designed our content-based image retrieval (CBIR) system using SIFT, SURF, SURF128 and ORB feature detector/descriptors and compared the performance of the system using each feature detector/descriptor. Our experiment confirmed that crowdsourcing can dramatically improve the CBIR system performance