Gender prediction from Tweets with convolutional neural networks: Notebook for PAN at CLEF 2018

19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018; Avignon; France; 10 September 2018 through 14 September 2018This paper presents a system1 developed for the author profiling task of PAN at CLEF 2018. The system utilizes style-based features to predict the gender information from the given tweets of each user. These features are automatically extracted by Convolutional Neural Networks (CNN). The system mainly depends on the idea that the informativeness of each tweet is not the same in terms of the gender of a user. Thus, the attention mechanism is included to the CNN outputs in order to discriminate the tweets carrying more information. Our architecture was able to obtain competitive results on three languages provided by the PAN 2018 author profiling challenge with an average accuracy of 75.1% on local runs and 70.23% on the submission run

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas a gran escala

Overview of the ImageCLEF 2018 Caption Prediction Tasks

The caption prediction task is in 2018 in its second edition after the task was first run in the same format in 2017. For 2018 the database was more focused on clinical images to limit diversity. As automatic methods with limited manual control were used to select images, there is still an important diversity remaining in the image data set. Participation was relatively stable compared to 2017. Usage of external data was restricted in 2018 to limit critical remarks regarding the use of external resources by some groups in 2017. Results show that this is a difficult task but that large amounts of training data can make it possible to detect the general topics of an image from the biomedical literature. For an even better comparison it seems important to filter the concepts for the images that are made available. Very general concepts (such as “medical image”) need to be removed, as they are not specific for the images shown, and also extremely rare concepts with only one or two examples can not really be learned. Providing more coherent training data or larger quantities can also help to learn such complex models

LIA@CLEF 2018: Mining events opinion argumentation from raw unlabeled Twitter data using convolutional neural network

International audienceSocial networks on the Internet are becoming increasingly important in our society. In recent years, this type of media, through communication platforms such as Twitter, has brought new research issues due to the massive size of data exchanged and the important number of ever-increasing users. In this context, the CLEF 2018 Mining opinion argumentation task aims to retrieve, for a specific event (festival name or topic), the most diverse argumentative microblogs from a large collection of tweets about festivals in different languages. In this paper, we propose a four-step approach for extracting argumentative microblogs related to a specific query (or event) while no reference data is provided

When humans and machines collaborate: Cross-lingual Label Editing in Wikidata

The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data

The Holistic Archival Personality Profiling Model (HAPPM): Comprehensive Data Integration for Personality Analysis

The traditional approach to biographical profiling, predominantly reliant on limited and fragmented datasets, has frequently resulted in superficial personality understandings. This is largely due to an overemphasis on official records and notable events, neglecting the rich tapestry of everyday experiences and personal interactions that significantly shape personalities. To address this shortcoming, this article introduces a multi-disciplinary methodology, The Holistic Archival Personality Profiling Model (HAPPM), which integrates a diverse array of archival materials, including personal correspondences, social media footprints, and family memorabilia. This approach involves digitizing various data forms, including handwritten documents, into machine-readable text, and then semantically classifying this data with biotags, chronotags, and geotags for organization within specific spatial and temporal contexts. Such comprehensive data aggregation establishes a more accurate space-time continuum for individuals, enhancing our understanding of their lives. The innovative aspect of HAPPM is the utilization of large language models to converse with the data, facilitating a more holistic representation of personalities. Preliminary results from applying HAPPM have shown its efficacy in uncovering previously unknown aspects of individual lives, offering insights into personal beliefs, daily routines, and social interactions. This has been validated through comparative analysis with existing biographical data, revealing a more complete and nuanced understanding of personalities. Therefore, HAPPM marks a significant advancement in personality profiling, capturing not only the grandiose but also the mundane, and offering a comprehensive tool for researchers and historians to explore the full spectrum of human experience