Linguistic resources can be populated with data through the use of such approaches as crowdsourcing and gamification when motivated people are involved. However, current crowdsourcing genre taxonomies lack the concept of cooperation, which is the principal element of modern video games and may potentially drive the annotators’ interest. This survey on crowdsourcing taxonomies and cooperation in linguistic resources provides recommendations on using cooperation in existent genres of crowdsourcing and an evidence of the efficiency of cooperation using a popular Russian linguistic resource created through crowdsourcing as an example.
There are other attempts to create a taxonomy of crowdsourcing genres. Zwass investigated the phenomena of @cite_12 and proposed a taxonomy of user-created digital content which includes the following: knowledge compendia, consumer reviews, multimedia content, blogs, mashups, virtual worlds. The resulted taxonomy appears to be too general and, since it was not intended, does not fit the natural language processing field perfectly.
@cite_12
2950536206 1998850479 2252123752 2100335205
Massive amounts of contributed content -- including traditional literature, blogs, music, videos, reviews and tweets -- are available on the Internet today, with authors numbering in many millions. Textual information, such as product or service reviews, is an important and increasingly popular type of content that is being used as a foundation of many trendy community-based reviewing sites, such as TripAdvisor and Yelp. Some recent results have shown that, due partly to their specialized topical nature, sets of reviews authored by the same person are readily linkable based on simple stylometric features. In practice, this means that individuals who author more than a few reviews under different accounts (whether within one site or across multiple sites) can be linked, which represents a significant loss of privacy. In this paper, we start by showing that the problem is actually worse than previously believed. We then explore ways to mitigate authorship linkability in community-based reviewing. We first attempt to harness the global power of crowdsourcing by engaging random strangers into the process of re-writing reviews. As our empirical results (obtained from Amazon Mechanical Turk) clearly demonstrate, crowdsourcing yields impressively sensible reviews that reflect sufficiently different stylometric characteristics such that prior stylometric linkability techniques become largely ineffective. We also consider using machine translation to automatically re-write reviews. Contrary to what was previously believed, our results show that translation decreases authorship linkability as the number of intermediate languages grows. Finally, we explore the combination of crowdsourcing and machine translation and report on the results. While multimedia and social computing research have used crowdsourcing techniques to annotate objects, actions, and scenes in social video sites like YouTube, little work has addressed the crowdsourcing of personal and social traits in online social video or social media content in general. In this paper, we address the problems of (1) crowdsourcing the annotation of first impressions of video bloggers (vloggers) personal and social traits in conversational YouTube videos, and (2) mining the impressions with the goal of modeling the interplay of different vlogger facets. First, we design a human annotation task to crowdsource impressions of vloggers that extends a tradition of studies of personality impressions with the addition of attractiveness and mood impressions. Second, we propose a probabilistic framework using Topic Models to discover prototypical impressions that are data driven, and that combine multiple facets of vloggers. Finally, we address the task of automatically predicting topic impressions using nonverbal and verbal content extracted from videos and comments. Our study of 442 YouTube vlogs and 2,210 annotations collected in Mechanical Turk supports recent literature showing the feasibility to crowdsource interpersonal human impression with comparable quality to what is reported in social psychology research, and provides insights on the interplay among human first impressions. We also show that topic models are useful to discover meaningful prototypical impressions that can be validated by humans, and that different topics can be predicted using different sources of information from vloggers’ nonverbal and verbal content, as well as comments from the audience. In this paper, we analyze a novel set of features for the task of automatic edit category classification. Edit category classification assigns categories such as spelling error correction, paraphrase or vandalism to edits in a document. Our features are based on differences between two versions of a document including meta data, textual and language properties and markup. In a supervised machine learning experiment, we achieve a micro-averaged F1 score of .62 on a corpus of edits from the English Wikipedia. In this corpus, each edit has been multi-labeled according to a 21-category taxonomy. A model trained on the same data achieves state-of-the-art performance on the related task of fluency edit classification. We apply pattern mining to automatically labeled edits in the revision histories of different Wikipedia articles. Our results suggest that high-quality articles show a higher degree of homogeneity with respect to their collaboration patterns as compared to random articles. When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle--they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence "Wal-Mart supply chain goes real time", how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that "Ciprofloxacin belongs to the quinolones group", how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge--an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.