pwc-archive/papers-with-abstracts huggingface dataset:
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment
Authors: Liam Watson, Anna Jurek-Loughrey, Barry Devereux, Brian Murphy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence, Sentiment Analysis, Word Embeddings

While there is a rich literature on the tracking of sentiment and emotion in texts, modelling the emotional trajectory of longer narratives, such as literary texts, poses new challenges. Previous work in the area of sentiment analysis has focused on using information from within a sentence to predict a valence value for that sentence. We propose to explore the influence of previous sentences on the sentiment of a given sentence. In particular, we investigate whether information present in a history of previous sentences can be used to predict a valence value for the following sentence. We explored both linear and non-linear models applied with a range of different feature combinations. We also looked at different context history sizes to determine what range of previous sentence context was the most informative for our models. We establish a linear relationship between sentence context history and the valence value of the current sentence and demonstrate that sentences in closer proximity to the target sentence are more informative. We show that the inclusion of semantic word embeddings further enriches our model predictions.
The Little Prince in 26 Languages: Towards a Multilingual Neuro-Cognitive Corpus
Authors: Sabrina Stehwien, Lena Henke, John Hale, Jonathan Brennan, Lars Meyer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: EEG, Electroencephalogram (EEG)

We present the Le Petit Prince Corpus (LPPC), a multi-lingual resource for research in (computational) psycho- and neurolinguistics. The corpus consists of the children{'}s story The Little Prince in 26 languages. The dataset is in the process of being built using state-of-the-art methods for speech and language processing and electroencephalography (EEG). The planned release of LPPC dataset will include raw text annotated with dependency graphs in the Universal Dependencies standard, a near-natural-sounding synthetic spoken subset as well as EEG recordings. We will use this corpus for conducting neurolinguistic studies that generalize across a wide range of languages, overcoming typological constraints to traditional approaches. The planned release of the LPPC combines linguistic and EEG data for many languages using fully automatic methods, and thus constitutes a readily extendable resource that supports cross-linguistic and cross-disciplinary research.
Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks
Authors: Billal Belainine, Fatiha Sadat, Mounir Boukadoum, Hakim Lounis
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis, Transfer Learning

In sentiment analysis, several researchers have used emoji and hashtags as specific forms of training and supervision. Some emotions, such as fear and disgust, are underrepresented in the text of social media. Others, such as anticipation, are absent. This research paper proposes a new dataset for complex emotion detection using a combination of several existing corpora in order to represent and interpret complex emotions based on the Plutchik{'}s theory. Our experiments and evaluations confirm that using Transfer Learning (TL) with a rich emotional corpus, facilitates the detection of complex emotions in a four-dimensional space. In addition, the incorporation of the rule on the reverse emotions in the model{'}s architecture brings a significant improvement in terms of precision, recall, and F-score.
Sensorimotor Norms for 506 Russian Nouns
Authors: Alex Miklashevsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Embodied cognitive science suggested a number of variables describing our sensorimotor experience associated with different concepts: modality experience rating (i.e., relationship between words and images of a particular perceptive modality{---}visual, auditory, haptic etc.), manipulability (the necessity for an object to interact with human hands in order to perform its function), vertical spatial localization. According to the embodied cognition theory, these semantic variables capture our mental representations and thus should influence word learning, processing and production. However, it is not clear how these new variables are related to such traditional variables as imageability, age of acquisition (AoA) and word frequency. In the presented database, normative data on the modality (visual, auditory, haptic, olfactory, and gustatory) ratings, vertical spatial localization of the object, manipulability, imageability, age of acquisition, and subjective frequency for 506 Russian nouns are collected. Factor analysis revealed four factors: (1) visual and haptic modality ratings were combined with imageability, manipulability and AoA; (2) word length, frequency and AoA; (3) olfactory modality was united with gustatory; (4) spatial localization only was included in the fourth factor. The database is available online together with a publication describing the method of data collection and data parameters (Miklashevsky, 2018).
Store Scientific Workflows Data in SSHOC Repository
Authors: Cesare Concordia, Carlo Meghini, Filippo Benedetti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management

Today scientific workflows are used by scientists as a way to define automated, scalable, and portable in-silico experiments. Having a formal description of an experiment can improve replicability and reproducibility of the experiment. However, simply storing and publishing the workflow may be not enough, an accurate management of provenance data generated during workflow life cycle is crucial to achieve reproducibility. This document presents the activity being carried out by CNR-ISTI in task 5.2 of the SSHOC project to add to the repository service developed in the task, functionalities to store, access and manage {`}workflow data{'} in order to improve replicability and reproducibility of e-science experiments.
Social Sciences and Humanities Pathway Towards the European Open Science Cloud
Authors: Francesca Di Donato, Monica Monachini, Maria Eskevich, Stefanie Pohle, Yoann Moranville, Suzanne Dumouchel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The paper presents a journey, which starts from various social sciences and humanities (SSH) Research Infrastructures in Europe and arrives at the comprehensive {``}ecosystem of infrastructures{''}, namely the European Open Science Cloud (EOSC). We will highlight how the SSH Open Science infrastructures contribute to the goal of establishing the EOSC. First, through the example of OPERAS, the European Research Infrastructure for Open Scholarly Communication in the SSH, to see how its services are conceived to be part of the EOSC and to address the communities{'} needs. The next two sections highlight collaboration practices between partners in Europe to build the SSH component of the EOSC and a SSH discovery platform, as a service of OPERAS and the EOSC. The last two sections will focus on an implementation network dedicated to SSH data fairification.
From the attic to the cloud: mobilization of endangered language resources with linked data
Authors: Sebastian Nordhoff
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

This paper describes a collection of 20k ELAN annotation files harvested from five different endangered language archives. The ELAN files form a very heterogeneous set, but the hierarchical configuration of their tiers allow, in conjunction with the tier content, to identify transcriptions, translations, and glosses. These transcriptions, translations, and glosses are queryable across archives. Small analyses of graphemes (transcription tier), grammatical and lexical glosses (gloss tier), and semantic concepts (translation tier) show the viability of the approach. The use of identifiers from OLAC, Wikidata and Glottolog allows for a better integration of the data from these archives into the Linguistic Linked Open Data Cloud.
Verbal Aggression as an Indicator of Xenophobic Attitudes in Greek Twitter during and after the Financial Crisis
Authors: Maria Pontiki, Maria Gavriilidou, Dimitris Gkoumas, Stelios Piperidis
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present a replication of a data-driven and linguistically inspired Verbal Aggression analysis framework that was designed to examine Twitter verbal attacks against predefined target groups of interest as an indicator of xenophobic attitudes during the financial crisis in Greece, in particular during the period 2013-2016. The research goal in this paper is to re-examine Verbal Aggression as an indicator of xenophobic attitudes in Greek Twitter three years later, in order to trace possible changes regarding the main targets, the types and the content of the verbal attacks against the same targets in the post crisis era, given also the ongoing refugee crisis and the political landscape in Greece as it was shaped after the elections in 2019. The results indicate an interesting rearrangement of the main targets of the verbal attacks, while the content and the types of the attacks provide valuable insights about the way these targets are being framed as compared to the respective dominant perceptions and stereotypes about them during the period 2013-2016.
Mining Wages in Nineteenth-Century Job Advertisements. The Application of Language Resources and Language Technology to study Economic and Social Inequality
Authors: Ruben Ros, Marieke van Erp, Auke Rijpma, Richard Zijdeman
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

For the analysis of historical wage development, no structured data is available. Job advertisements, as found in newspapers can provide insights into what different types of jobs paid, but require language technology to structure in a format conducive to quantitative analysis. In this paper, we report on our experiments to mine wages from 19th century newspaper advertisements and detail the challenges that need to be overcome to perform a socio-economic analysis of textual data sources.
LR4SSHOC: The Future of Language Resources in the Context of the Social Sciences and Humanities Open Cloud
Authors: Daan Broeder, Maria Eskevich, Monica Monachini
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper outlines the future of language resources and identifies their potential contribution for creating and sustaining the social sciences and humanities (SSH) component of the European Open Science Cloud (EOSC).
EOSC as a game-changer in the Social Sciences and Humanities research activities
Authors: Donatella Castelli
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper aims to give some insights on how the European Open Science Cloud (EOSC) will be able to influence the Social Sciences and Humanities (SSH) sector, thus paving the way towards innovation. Points of discussion on how the LRs and RIs community can contribute to the revolution in the practice of research areas are provided.
Stretching Disciplinary Boundaries in Language Resource Development and Use: a Linguistic Data Consortium Position Paper
Authors: Christopher Cieri
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Position

Given the persistent gap between demand and supply, the impetus to reuse language resources is great. Researchers benefit from building upon the work of others including reusing data, tools and methodology. Such reuse should always consider the original intent of the language resource and how that impacts potential reanalysis. When the reuse crosses disciplinary boundaries, the re-user also needs to consider how research standards that differ between social science and humanities on the one hand and human language technologies on the other might lead to differences in unspoken assumptions. Data centers that aim to support multiple research communities have a responsibility to build bridges across disciplinary divides by sharing data in all directions, encouraging re-use and re-sharing and engaging directly in research that improves methodologies.
Crossing the SSH Bridge with Interview Data
Authors: Henk van den Heuvel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

Spoken audio data, such as interview data, is a scientific instrument used by researchers in various disciplines crossing the boundaries of social sciences and humanities. In this paper, we will have a closer look at a portal designed to perform speech-to-text conversion on audio recordings through Automatic Speech Recognition (ASR) in the CLARIN infrastructure. Within the cluster cross-domain EU project SSHOC the potential value of such a linguistic tool kit for processing spoken language recording has found uptake in a webinar about the topic, and in a task addressing audio analysis of panel survey data. The objective of this contribution is to show that the processing of interviews as a research instrument has opened up a fascinating and fruitful area of collaboration between Social Sciences and Humanities (SSH).
Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the U.S.
Authors: Bart Desmet, Julia Porcino, Ayah Zirikly, Denis Newman-Griffis, Guy Divita, Elizabeth Rasch
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Document Ranking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA{'}s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.
FRAQUE: a FRAme-based QUEstion-answering system for the Public Administration domain
Authors: Martina Miliani, Lucia C. Passaro, Aless Lenci, ro
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Question Answering, Word Embeddings

In this paper, we propose FRAQUE, a question answering system for factoid questions in the Public administration domain. The system is based on semantic frames, here intended as collections of slots typed with their possible values. FRAQUE queries unstructured textual data and exploits the potential of different approaches: it extracts pattern elements from texts which are linguistically analyzed through statistical methods.FRAQUE allows Italian users to query vast document repositories related to the domain of Public Administration. Given the statistical nature of most of its components such as word embeddings, the system allows for a flexible domain and language adaptation process. FRAQUE{'}s goal is to associate questions with frames stored into a Knowledge Graph along with relevant document passages, which are returned as the answer.
Enhancing Job Searches in Mexico City with Language Technologies
Authors: Gerardo Sierra Mart{\'\i}nez, Gemma Bel-Enguix, Helena G{\'o}mez-Adorno, Juan Manuel Torres Moreno, Tonatiuh Hern{\'a}ndez-Garc{\'\i}a, Julio V Guadarrama-Olvera, Jes{\'u}s-Germ{\'a}n Ortiz-Barajas, {\'A}ngela Mar{\'\i}a Rojas, Tomas Damerau, Soledad Arag{\'o}n Mart{\'\i}nez
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management

In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagn{\'o}stico de Competencias Demandadas), a system developed by Mexico City{'}s Ministry of Labor and Employment Promotion (STyFE: Secretar{\'\i}a de Trabajo y Fomento del Empleo de la Ciudad de M{\'e}xico) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingenier{\'\i}a Ling{\"u}{\'\i}stica) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe{'}s job vacancies system{'}s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.
Research \& Innovation Activities' Impact Assessment: The Data4Impact System
Authors: Ioanna Grypari, Dimitris Pappas, Natalia Manola, Haris Papageorgiou
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Cat. 2 Show-case: We present the Data4Impact (D4I) platform, a novel end-to-end system for evidence-based, timely and accurate monitoring and evaluation of research and innovation (R{\&}I) activities. Using the latest technological advances in Human Language Technology (HLT) and our data-driven methodology, we build a novel set of indicators in order to track funded projects and their impact on science, the economy and the society as a whole, during and after the project life-cycle. We develop our methodology by targeting Health-related EC projects from 2007 to 2019 to produce solutions that meet the needs of stakeholders (mainly policy-makers and research funders). Various D4I text analytics workflows process datasets and their metadata, extract valuable insights and estimate intermediate results and metrics, culminating in a set of robust indicators that the users can interact with through our dashboard, the D4I Monitor (available at monitor.data4impact.eu). Therefore, our approach, which can be generalized to different contexts, is multidimensional (technology, tools, indicators, dashboard) and the resulting system can provide an innovative solution for public administrators in their policy-making needs related to RDI funding allocation.
The Austrian Language Resource Portal for the Use and Provision of Language Resources in a Language Variety by Public Administration -- a Showcase for Collaboration between Public Administration and a University
Authors: Barbara Heinisch, Vesna Lu{\v{s}}icky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

The Austrian Language Resource Portal (Sprachressourcenportal {\"O}sterreichs) is Austria{'}s central platform for language resources in the area of public administration. It focuses on language resources in the Austrian variety of the German language. As a product of the cooperation between a public administration body and a university, the Portal contains various language resources (terminological resources in the public administration domain, a language guide, named entities based on open public data, translation memories, etc.). German is a pluricentric language that considerably varies in the domain of public administration due to different public administration systems. Therefore, the Austrian Language Resource Portal stresses the importance of language resources specific to a language variety, thus paving the way for the re-use of variety-specific language data for human language technology, such as machine translation training, for the Austrian standard variety.
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing
Authors: Doaa Samy, Jer{\'o}nimo Arenas-Garc{\'\i}a, David P{\'e}rez-Fern{\'a}ndez
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Navigate, Semantic Similarity, Semantic Textual Similarity, Topic Models, Word Embeddings

Legal-ES is an open source resource kit for legal Spanish. It consists of a large scale Spanish corpus of open legal texts and different kinds of language models including word embeddings and topic models. The corpus includes over 1000 million words covering a collection of legislative and administrative open access documents in Spanish from different sources representing international, national and regional entities. The corpus is pre-processed and tokenized using Spacy. For the word embeddings, gensim was used on the collection of tokens, producing a representation space that is especially suited to reflect the inherent characteristics of the legal domain. We calculate also topic models to obtain a convenient tool to understand the main topics in the corpus and to navigate through the documents exploiting the semantic similarity among documents. We will analyse the time structure of a dynamic topic model to infer changes in the legal production of Spanish jurisdiction that have occurred over the analysed time framework.
Dating and Stratifying a Historical Corpus with a Bayesian Mixture Model
Authors: Oliver Hellwig
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper introduces and evaluates a Bayesian mixture model that is designed for dating texts based on the distributions of linguistic features. The model is applied to the corpus of Vedic Sanskrit the historical structure of which is still unclear in many details. The evaluation concentrates on the interaction between time, genre and linguistic features, detecting those whose distributions are clearly coupled with the historical time. The evaluation also highlights the problems that arise when quantitative results need to be reconciled with philological insights.
Automatic Construction of Aramaic-Hebrew Translation Lexicon
Authors: Chaya Liebeskind, Shmuel Liebeskind
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence, Translation, Word Alignment

Aramaic is an ancient Semitic language with a 3,000 year history. However, since the number of Aramaic speakers in the world hasdeclined, Aramaic is in danger of extinction. In this paper, we suggest a methodology for automatic construction of Aramaic-Hebrew translation Lexicon. First, we generate an initial translation lexicon by a state-of-the-art word alignment translation model. Then,we filter the initial lexicon using string similarity measures of three types: similarity between terms in the target language, similarity between a source and a target term, and similarity between terms in the source language. In our experiments, we use a parallel corporaof Biblical Aramaic-Hebrew sentence pairs and evaluate various string similarity measures for each type of similarity. We illustratethe empirical benefit of our methodology and its effect on precision and F1. In particular, we demonstrate that our filtering methodsignificantly exceeds a filtering approach based on the probability scores given by a state-of-the-art word alignment translation model.