ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign

Date-Driven Approach For Thematic Role Extraction

Thematic role labeling has been an area of interest in several domains of natural language processing such as language generation and information retrieval. Extracting thematic roles efficiently and with minimal supervision affects all the above areas. Most of the existing computational methods for thematic role extraction are highly dependent on human annotated corpora, and are driven by the rules generated by supervised processes. In this work I extracted thematic roles using a data-driven approach. I built an unsupervised language generation model inspired from the ADIOS model, to learn recurring substructures from language, and with minimal supervision learned the rules that are needed to identify thematic roles. I tested the consistency of the sub-structures encoding thematic role information over PropBank annotated sentences. Results indicated that sub-structures consistently hold semantic role information, and the method robustly showed that thematic role information could be extracted with minimal supervision

Social Networks are Encoded in Language

Knowledge regarding social information is thought to be derived from many different sources, such as interviews and formal relationships. Social networks can likewise be generated from such external information. Recent work has demonstrated that statistical linguistic data can explain findings thought to be explained by external factors alone, such as perceptual relations. The current study explored whether language implicitly comprises information that allows for extracting social networks, by testing the hypothesis that individuals who are socially related together are linguistically talked about together, as well as the hypothesis that individuals who are socially related more are talked about more. In the first analysis using first-order cooccurrences of names of characters in the Harry Potter novels we found that an MDS solution correlated with the actual social network of characters as rated by humans. In a second study using higher-order co-occurrences, a latent semantic analysis (LSA) space was trained on all seven Harry Potter novels. LSA cosine values for all character pairs were obtained, marking their semantic similarity. Again, an MDS analysis comparing the LSA data with the actual social relationships yielded a significant bidimensional regression. These results demonstrate that linguistic information indeed encodes social relationship information and show that implicit information within language can generate social networks

A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language

Semantic roles play an important role in extracting knowledge from text. Current unsupervised approaches utilize features from grammar structures, to induce semantic roles. The dependence on these grammars, however, makes it difficult to adapt to noisy and new languages. In this paper we develop a data-driven approach to identifying semantic roles, the approach is entirely unsupervised up to the point where rules need to be learned to identify the position the semantic role occurs. Specifically we develop a modified-ADIOS algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and use these grammar structures to learn the rules for identifying the semantic roles based on the context in which the grammar structures appeared. The results obtained are comparable with the current state-of-art models that are inherently dependent on human annotated data