pwc-archive/papers-with-abstracts huggingface dataset:
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
Authors: Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
Date: 2020-05-15 00:00:00
Tasks: General Classification

3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the design of optical networks in the form of multiple diffractive layers that are trained using deep learning to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which are used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique machine vision framework merges the power of deep learning with the spatial and spectral processing capabilities of diffractive networks, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive optical networks.
Keynote Abstract: Too soon? The limitations of AI for event data
Authors: Clionadh Raleigh
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Not all conflict datasets offer equal levels of coverage, depth, use-ability, and content. A review of the inclusion criteria, methodology, and sourcing of leading publicly available conflict datasets demonstrates that there are significant discrepancies in the output produced by ostensibly similar projects. This keynote will question the presumption of substantial overlap between datasets, and identify a number of important gaps left by deficiencies across core criteria for effective conflict data collection and analysis.
Keynote Abstract: Current Open Questions for Operational Event Data
Authors: Philip A. Schrodt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this brief keynote, I will address what I see as five majorissues in terms of development for operational event datasets (that is, event data intended for real time monitoringand forecasting, rather than purely for academic research).
Analyzing ELMo and DistilBERT on Socio-political News Classification
Authors: Berfu B{\"u}y{\"u}k{\"o}z, Ali H{\"u}rriyeto{\u{g}}lu, Arzucan {\"O}zg{\"u}r
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, News Classification, Sentiment Analysis

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A {''}cross-context{''} setting is enabled using test sets that are distinct from the training data. The models are fine-tuned and fed into a Feed-Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM). Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30{\%} smaller and 83{\%} faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.
Text Categorization for Conflict Event Annotation
Authors: Fredrik Olsson, Magnus Sahlgren, Fehmi ben Abdesslem, Ariel Ekgren, Kristine Eck
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Categorization

We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3{\%} to 99.8{\%} F-score.
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study
Authors: Jakub Piskorski, Guillaume Jacquet
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Word Embeddings

Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering. This paper reports on some preliminary experiments of comparing various linguistically-lightweight approaches for fine-grained event classification based on short text snippets reporting on events. In particular, we compare the performance of a TF-IDF-weighted character n-gram SVM-based model versus SVMs trained on various of-the-shelf pre-trained word embeddings (GloVe, BERT, FastText) as features. We exploit a relatively large event corpus consisting of circa 610K short text event descriptions classified using a 25-event categories that cover political violence and protest events. The best results, i.e., 83.5{\%} macro and 92.4{\%} micro F1 score, were obtained using the TF-IDF-weighted character n-gram model.
Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes
Authors: Benjamin Radford
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: coreference-resolution, Coreference Resolution, Cross Document Coreference Resolution, Event Coreference Resolution

Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references{---}recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines{'} texts and publication dates.
Conflict Event Modelling: Research Experiment and Event Data Limitations
Authors: Matina Halkia, Stefano Ferri, Michail Papazoglou, Marie-Sophie Van Damme, Dimitrios Thomakos
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union{'}s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.
Supervised Event Coding from Text Written in Arabic: Introducing Hadath
Authors: Javier Osorio, Alej Reyes, Alej Beltr{\'a}n, ro, Atal Ahmadzai
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This article introduces Hadath, a supervised protocol for coding event data from text written in Arabic. Hadath contributes to recent efforts in advancing multi-language event coding using computer-based solutions. In this application, we focus on extracting event data about the conflict in Afghanistan from 2008 to 2018 using Arabic information sources. The implementation relies first on a Machine Learning algorithm to classify news stories relevant to the Afghan conflict. Then, using Hadath, we implement the Natural Language Processing component for event coding from Arabic script. The output database contains daily geo-referenced information at the district level on who did what to whom, when and where in the Afghan conflict. The data helps to identify trends in the dynamics of violence, the provision of governance, and traditional conflict resolution in Afghanistan for different actors over time and across space.
Protest Event Analysis: A Longitudinal Analysis for Greece
Authors: Konstantina Papanikolaou, Haris Papageorgiou
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The advent of Big Data has shifted social science research towards computational methods. The volume of data that is nowadays available has brought a radical change in traditional approaches due to the cost and effort needed for processing. Knowledge extraction from heterogeneous and ample data is not an easy task to tackle. Thus, interdisciplinary approaches are necessary, combining experts of both social and computer science. This paper aims to present a work in the context of protest analysis, which falls into the scope of Computational Social Science. More specifically, the contribution of this work is to describe a Computational Social Science methodology for Event Analysis. The presented methodology is generic in the sense that it can be applied in every event typology and moreover, it is innovative and suitable for interdisciplinary tasks as it incorporates the human-in-the-loop. Additionally, a case study is presented concerning Protest Analysis in Greece over the last two decades. The conceptual foundation lies mainly upon claims analysis, and newspaper data were used in order to map, document and discuss protests in Greece in a longitudinal perspective.
Event Clustering within News Articles
Authors: Faik Kerem {\"O}rs, S{\"u}veyda Yeniterzi, Reyyan Yeniterzi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Clustering, Sentence

This paper summarizes our group{'}s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.
Enriching Historic Photography with Structured Data using Image Region Segmentation
Authors: Taylor Arnold, Lauren Tilton
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: object-detection, Object Detection

Cultural institutions such as galleries, libraries, archives and museums continue to make commitments to large scale digitization of collections. An ongoing challenge is how to increase discovery and access through structured data and the semantic web. In this paper we describe a method for using computer vision algorithms that automatically detect regions of {``}stuff{''} {---} such as the sky, water, and roads {---} to produce rich and accurate structured data triples for describing the content of historic photography. We apply our method to a collection of 1610 documentary photographs produced in the 1930s and 1940 by the FSA-OWI division of the U.S. federal government. Manual verification of the extracted annotations yields an accuracy rate of 97.5{\%}, compared to 70.7{\%} for relations extracted from object detection and 31.5{\%} for automatically generated captions. Our method also produces a rich set of features, providing more unique labels (1170) than either the captions (1040) or object detection (178) methods. We conclude by describing directions for a linguistically-focused ontology of region categories that can better enrich historical image data. Open source code and the extracted metadata from our corpus are made available as external resources.
Interlinking Iconclass Data with Concepts of Art \& Architecture Thesaurus
Authors: Anna Breit
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Iconclass, being a a well established classification system, could benefit from interconnections with other ontologies in order to semantically enrich its content. This work presents a disambiguating and interlinking approach which is used to map Iconclass Subjects to concepts of the Art and Architecture Thesaurus. In a preliminary evaluation, the system is able to produce promising predictions, though the task is highly challenging due to conceptual and schema heterogeneity. Several algorithmic improvements for this specific interlinking task, as well as and future research directions are suggestions. The produced mappings, as well as the source code and additional information can be found at https://github.com/annabreit/taxonomy-interlinking.
Toward the Automatic Retrieval and Annotation of Outsider Art images: A Preliminary Statement
Authors: John Roberto, Diego Ortego, Brian Davis
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Retrieval

The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting. Specifically, we explore whether is it possible to classify non-traditional artistic styles by using the same features that are used for classifying traditional styles? Our research question is motivated by two facts. First, art historians state that non-traditional styles are influenced by factors {``}outside{''} of the world of art. Second, some studies have shown that several artistic styles confound certain classification techniques. Following current approaches to style prediction, this paper utilises Deep Learning methods to encode image features. Our preliminary experiments have provided motivation to think that, as is the case with traditional styles, Outsider Art can be computationally modelled with objective means by using training datasets and CNN models. Nevertheless, our results are not conclusive due to the lack of a large available dataset on Outsider Art. Therefore, at the end of the paper, we have mapped future lines of action, which include the compilation of a large dataset of Outsider Art images and the creation of an ontology of Outsider Art.
Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis
Authors: Christian Bartz, Nitisha Jain, Ralf Krestel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cultural Vocal Bursts Intensity Prediction, Descriptive

Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.
Towards a Comprehensive Assessment of the Quality and Richness of the Europeana Metadata of food-related Images
Authors: Yalemisew Abgaz, Amelie Dorn, Jose Luis Preza Diaz, Gerda Koch
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Semantic enrichment of historical images to build interactive AI systems for the Digital Humanities domain has recently gained significant attention. However, before implementing any semantic enrichment tool for building AI systems, it is also crucial to analyse the quality and richness of the existing datasets and understand the areas where semantic enrichment is most required. Here, we propose an approach to conducting a preliminary analysis of selected historical images from the Europeana platform using existing linked data quality assessment tools. The analysis targets food images by collecting metadata provided from curators such as Galleries, Libraries, Archives and Museums (GLAMs) and cultural aggregators such as Europeana. We identified metrics to evaluate the quality of the metadata associated with food-related images which are harvested from the Europeana platform. In this paper, we present the food-image dataset, the associated metadata and our proposed method for the assessment. The results of our assessment will be used to guide the current effort to semantically enrich the images and build high-quality metadata using Computer Vision.
Line-a-line: A Tool for Annotating Word-Alignments
Authors: Maria Skeppstedt, Magnus Ahltorp, Gunnar Eriksson, Rickard Domeij
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Multilingual Word Embeddings, Sentence, Word Alignment, Word Embeddings

We here describe line-a-line, a web-based tool for manual annotation of word-alignments in sentence-aligned parallel corpora. The graphical user interface, which builds on a design template from the Jigsaw system for investigative analysis, displays the words from each sentence pair that is to be annotated as elements in two vertical lists. An alignment between two words is annotated by drag-and-drop, i.e. by dragging an element from the left-hand list and dropping it on an element in the right-hand list. The tool indicates that two words are aligned by lines that connect them and by highlighting associated words when the mouse is hovered over them. Line-a-line uses the efmaral library for producing pre-annotated alignments, on which the user can base the manual annotation. The tool is mainly planned to be used on moderately under-resourced languages, for which resources in the form of parallel corpora are scarce. The automatic word-alignment functionality therefore also incorporates information derived from non-parallel resources, in the form of pre-trained multilingual word embeddings from the MUSE library.
Overview of the Fourth BUCC Shared Task: Bilingual Dictionary Induction from Comparable Corpora
Authors: Reinhard Rapp, Pierre Zweigenbaum, Serge Sharoff
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The shared task of the 13th Workshop on Building and Using Comparable Corpora was devoted to the induction of bilingual dictionaries from comparable rather than parallel corpora. In this task, for a number of language pairs involving Chinese, English, French, German, Russian and Spanish, the participants were supposed to determine automatically the target language translations of several thousand source language test words of three frequency ranges. We describe here some background, the task definition, the training and test data sets and the evaluation used for ranking the participating systems. We also summarize the approaches used and present the results of the evaluation. In conclusion, the outcome of the competition are the results of a number of systems which provide surprisingly good solutions to the ambitious problem.
Constructing a Bilingual Corpus of Parallel Tweets
Authors: Hamdy Mubarak, Sabit Hassan, Ahmed Abdelali
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Author Profiling, Machine Translation, Translation

In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets{---}tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. In this paper, we introduce a generic method for collecting parallel tweets. Using this method, we collect a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabictweets regularly. Since our method is generic, it can also be used for collecting parallel tweets that cover less-resourced languages such as Serbian and Urdu. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. This latter information can also be useful for author profiling tasks.