MICO - Media in Context

Aichroth, Patrick; Weigel, Christian; Kurz, Thomas; Stadler, Horst; Drewes, Frank; Björklund, Johanna; Schlegel, Kai; Berndl, Emanuel; Pérez, Antonio J. Salguero; Bowyer, Alex; Volpini, Andrea

doi:10.1109/icmew.2015.7169827

Cited by 4 publications

(3 citation statements)

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“…Moreover, the actual textual content part seems to be perceived as a trife and cut off from the equation of contribution assessments. Apparently, that self-contradicts with their stated guidelines (e.g., in SE 6 ) for achieving standard content quality. Yet, that failed to have dedicated utilities that directly deal with textual content quality.…”

Section: Motivation Challenges and Application Areasmentioning

confidence: 93%

“…Given that there are ever emerging semantic technologies and growing efforts to advance web content searchability. Recently, cross-media analysis platforms such as MICO [6] and EUMSSI [7] have been released. During the early stages (first milestones of the MICO project) of the MICO's platform architecture, we contributed text analysis components as a project partner [8].…”

Section: Motivation Challenges and Application Areasmentioning

confidence: 99%

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Expertise Detection in Crowdsourcing Forums Using the Composition of Latent Topics and Joint Syntactic–Semantic Cues

Woldemariam

2021

SN COMPUT. SCI.

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We develop an NLP method for inferring potential contributors among multitude of users within crowdsourcing forums (CSFs). The method basically provides a way to predict expertise from their structures (syntax–semantic patterns) when crowdsourced votes are unavailable. It primarily deals with tackling core adverse conditions, which hinder the identification of crowds’ expertise levels, and standardization of measuring linguistic quality of crowdsourced text. To solve the former, an expertise estimation and linguistic feature annotation algorithm is developed. To approach the later, a comprehensive linguistic characterization of crowdsourced text, along with extensive joint syntax–punctuation analyses, have been carried out. The entire corpora are comprised of approximately 8 different domains, 3 million and 50,000 sentences, and 32 million and 90,000 words, contributed by a crowd of 50,000 users. The analyses revealed six major linguistic patterns, identified on the basis of ordered lists of structural (syntactic) categories, learned from grammatical constructions, practiced by major groups of experts. In addition, nine different text-oriented expertise dimensions are identified, as crucial steps towards establishing standard linguistic-based expertise-framework for most CSFs. Potentially, the resulting framework simplifies the measurement of crowds’ proficiency, in those particular forums, where crowds’ tasks (e.g., answering questions, technically discerning deep features within images of galaxies for classifying them into certain categories) are intimately connected with their writing (e.g., describing answers illustratively, expressing complex phenomena observed in classified images). Moreover, wide varieties of linguistic annotations: latent topic annotations, named entities, syntactic and punctuation annotations, semantic and character set annotations, word and character n-grams (n = 2 and 3) annotations, are extracted. That is for building baseline and enhanced versions of expertise models (about 20 different models built). The successive achievements of enhancing baseline models, with iteratively adding linguistic feature annotations in a two-stage enhancement process, indicate the adaptability of the learned models.

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Section: Motivation Challenges and Application Areasmentioning

confidence: 93%

Section: Motivation Challenges and Application Areasmentioning

confidence: 99%

Expertise Detection in Crowdsourcing Forums Using the Composition of Latent Topics and Joint Syntactic–Semantic Cues

Woldemariam

2021

SN COMPUT. SCI.

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“…Its outcome is now in use by the BBC (BBC, 2015), however it is not publicly available. More recently, the EU funded Media in Context (MiCO) project (Aichroth et al, 2015). This project aimed at accomplishing a media analysis platform for multimodal media that supports customized workflows leveraging on assorted open and closed source content analysis tools.…”

Section: Prior Workmentioning

confidence: 99%

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

2019

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Welcome to the third edition of LaTeCH-CLfL-which also is the thirteenth edition of LaTeCH and eighth of CLfL. We have had fun preparing the workshop, and we will be happy if you have fun attending (or at least reading the workshop papers:). Please visit the website at https://sighum.wordpress.com/events/latech-clfl-2019/ where you will find the workshop presentations, among other things. The papers cover, as usual, topics which you will not easily find at regular NLP conferences. The authors take on literary texts, including drama and poetry, and more generally literary study; historical texts; ancient or otherwise old languages; government documents; code switching; and more. Last but certainly not least, we will have an invited talk. Ian Milligan, a historian, has a deep interest in Digital Humanities, and understands the role on Natural Language Processing in his discipline. It is our pleasant duty to thank the authors: there would be no workshop without you. Nor without the program committee, to whom we are ever so grateful for their thorough and helpful reviews.

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