pwc-archive/papers-with-abstracts huggingface dataset:
Localising the Clinical Terminology SNOMED CT by Semi-automated Creation of a German Interface Vocabulary
Authors: Stefan Schulz, Larissa Hammer, David Hashemian-Nik, Markus Kreuzthaler
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Retrieval, Text Generation

Medical language exhibits great variations regarding users, institutions and language registers. With large parts of clinical documents in free text, NLP is playing a more and more important role in unlocking re-usable and interoperable meaning from medical records. This study describes the architectural principles and the evolution of a German interface vocabulary, combining machine translation with human annotation and rule-based term generation, yielding a resource with 7.7 million raw entries, each of which linked to the reference terminology SNOMED CT, an international standard with about 350 thousand concepts. The purpose is to offer a high coverage of medical jargon in order to optimise terminology grounding of clinical texts by text mining systems. The core resource is a manually curated table of English-to-German word and chunk translations, supported by a set of language generation rules. The work describes a workflow consisting the enrichment and modification of this table with human and machine efforts, manually enriched by grammarspecific tags. Top-down and bottom-up methods for terminology population used in parallel. The final interface terms are produced by a term generator, which creates one-to-many German variants per SNOMED CT English description. Filtering against a large collection of domain terminologies and corpora drastically reduces the size of the vocabulary in favour of more realistic terms or terms that can reasonably be expected to match clinical text passages within a text-mining pipeline. An evaluation was performed by a comparison between the current version of the German interface vocabulary and the English description table of the SNOMED CT International release. An exact term matching was performed with a small parallel corpus constituted by text snippets from different clinical documents. With overall low retrieval parameters (with F-values around 30{\%}), the performance of the German language scenario reaches 80 {--} 90{\%} of the English one. Interestingly, annotations are slightly better with machine-translated (German {--} English) texts, using the International SNOMED CT resource only.
Transfer learning applied to text classification in Spanish radiological reports
Authors: Pilar L{\'o}pez {\'U}beda, Manuel Carlos D{\'\i}az-Galiano, L. Alfonso Urena Lopez, Maite Martin, Teodoro Mart{\'\i}n-Noguerol, Antonio Luna
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, text-classification, Text Classification, Transfer Learning

Pre-trained text encoders have rapidly advanced the state-of-the-art on many Natural Language Processing tasks. This paper presents the use of transfer learning methods applied to the automatic detection of codes in radiological reports in Spanish. Assigning codes to a clinical document is a popular task in NLP and in the biomedical domain. These codes can be of two types: standard classifications (e.g. ICD-10) or specific to each clinic or hospital. In this study we show a system using specific radiology clinic codes. The dataset is composed of 208,167 radiology reports labeled with 89 different codes. The corpus has been evaluated with three methods using the BERT model applied to Spanish: Multilingual BERT, BETO and XLM. The results are interesting obtaining 70{\%} of F1-score with a pre-trained multilingual model.
Automated Processing of Multilingual Online News for the Monitoring of Animal Infectious Diseases
Authors: Sarah Valentin, Renaud Lancelot, Mathieu Roche
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated system which monitors the web for monitoring and detecting emerging animal infectious diseases. The tool automatically collects news via customised multilingual queries, classifies them and extracts epidemiological information. We detail the processing of multilingual online sources by PADI-web and analyse the translated outputs in a case study
Prototypes and Recognition of Self in Depictions of Christ
Authors: Carla Sophie Lembke, Per Olav Folger{\o}, Alf Edgar Andresen, Christer Johansson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present a study on prototype effects. We designed an experiment investigating the effect of adapting a prototypical image towards more human, male or female, prototypes, and additionally investigating the effect of self-recognition in a manipulated image. Results show that decisions are affected by prototypicality, but we find less evidence that self-recognition further enhances perceptions of attractiveness. This study has implications for the psychological perception of faces, and may contribute to the study of Christian imagery.
Analysis of Body Behaviours in Human-Human and Human-Robot Interactions
Authors: Taiga Mori, Kristiina Jokinen, Yasuharu Den
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We conducted preliminary comparison of human-robot (HR) interaction with human-human (HH) interaction conducted in English and in Japanese. As the result, body gestures increased in HR, while hand and head gestures decreased in HR. Concerning hand gesture, they were composed of more diverse and complex forms, trajectories and functions in HH than in HR. Moreover, English speakers produced 6 times more hand gestures than Japanese speakers in HH. Regarding head gesture, even though there was no difference in the frequency of head gestures between English speakers and Japanese speakers in HH, Japanese speakers produced slightly more nodding during the robot{'}s speaking than English speakers in HR. Furthermore, positions of nod were different depending on the language. Concerning body gesture, participants produced body gestures mostly to regulate appropriate distance with the robot in HR. Additionally, English speakers produced slightly more body gestures than Japanese speakers.
Automatic Detection and Classification of Head Movements in Face-to-Face Conversations
Authors: Patrizia Paggio, Manex Agirrezabal, Bart Jongejan, Costanza Navarretta
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification

This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.
``You move THIS!'': Annotation of Pointing Gestures on Tabletop Interfaces in Low Awareness Situations
Authors: Dimitra Anastasiou, Hoorieh Afkari, Val{\'e}rie Maquil
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper analyses pointing gestures during low awareness situations occurring in a collaborative problem-solving activity implemented on an interactive tabletop interface. Awareness is considered as crucial requirement to support fluid and natural collaboration. We focus on pointing gestures as strategy to maintain awareness. We describe the results from a user study with five groups, each group consisting of three participants, who were asked to solve a task collaboratively on a tabletop interface. The ideal problem-solving solution would have been, if the three participants had been fully aware of what their personal area is depicting and had communicated this properly to the peers. However, often some participants are hesitant due to lack of awareness, some other want to take the lead work or expedite the process, and therefore pointing gestures to others{'} personal areas arise. Our results from analyzing a multimodal corpus of 168.68 minutes showed that in 95{\%} of the cases, one user pointed to the personal area of the other, while in a few cases (3{\%}) a user not only pointed, but also performed a touch gesture on the personal area of another user. In our study, the mean for such pointing gestures in low awareness situations per minute and for all groups was M=1.96, SD=0.58.
Improving Sentiment Analysis with Biofeedback Data
Authors: Daniel Schl{\"o}r, Albin Zehe, Konstantin Kobs, Blerta Veseli, Franziska Westermeier, Larissa Br{\"u}bach, Daniel Roth, Marc Erich Latoschik, Andreas Hotho
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Emotion Recognition, Sentence, Sentiment Analysis

Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.
An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training
Authors: Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik, Motaz Saad, Richard Johansson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: 8k, Arabic Sentiment Analysis, Sentiment Analysis

As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions reviews. For good social media sentiment analysis, good quality resources are needed, and the lack of these resources is particularly evident for languages other than English, in particular Arabic. The available Arabic resources lack of from either the size of the corpus or the quality of the annotation. In this paper, we present an Arabic Sentiment Analysis Corpus collected from Twitter, which contains 36K tweets labelled into positive and negative. We employed distant supervision and self-training approaches into the corpus to annotate it. Besides, we release an 8K tweets manually annotated as a gold standard. We evaluated the corpus intrinsically by comparing it to human classification and pre-trained sentiment analysis models, Moreover, we apply extrinsic evaluation methods exploiting sentiment analysis task and achieve an accuracy of 86{\%}.
Building a Corpus of Qatari Arabic Expressions
Authors: Sara Al-Mulla, Wajdi Zaghouani
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The current Arabic natural language processing resources are mainly build to address the Modern Standard Arabic (MSA), while we witnessed some scattered efforts to build resources for various Arabic dialects such as the Levantine and the Egyptian dialects. We observed a lack of resources for Gulf Arabic and especially the Qatari variety. In this paper, we present the first Qatari idioms and expression corpus of 1000 entries. The corpus was created from on-line and printed sources in addition to transcribed recorded interviews. The corpus covers various Qatari traditional expressions and idioms. To this end, audio recordings were collected from interviews and an online survey questionnaire was conducted to validate our data. This corpus aims to help advance the dialectal Arabic Speech and Natural Language Processing tools and applications for the Qatari dialect.
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
Authors: Ibrahim Abu Farha, Walid Magdy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Arabic Sentiment Analysis, Sarcasm Detection, Sentiment Analysis

Sarcasm is one of the main challenges for sentiment analysis systems. Its complexity comes from the expression of opinion using implicit indirect phrasing. In this paper, we present ArSarcasm, an Arabic sarcasm detection dataset, which was created through the reannotation of available Arabic sentiment analysis datasets. The dataset contains 10,547 tweets, 16{\%} of which are sarcastic. In addition to sarcasm the data was annotated for sentiment and dialects. Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators{'} biases. Experiments show the degradation of state-of-the-art sentiment analysers when faced with sarcastic content. Finally, we train a deep learning model for sarcasm detection using BiLSTM. The model achieves an F1 score of 0.46, which shows the challenging nature of the task, and should act as a basic baseline for future research on our dataset.
Overview of OSACT4 Arabic Offensive Language Detection Shared Task
Authors: Hamdy Mubarak, Kareem Darwish, Walid Magdy, Tamer Elsayed, Hend Al-Khalifa
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4). There were two subtasks, namely: Subtask A, involving the detection of offensive language, which contains unacceptable or vulgar content in addition to any kind of explicit or implicit insults or attacks against individuals or groups; and Subtask B, involving the detection of hate speech, which contains insults or threats targeting a group based on their nationality, ethnicity, race, gender, political or sport affiliation, religious belief, or other common characteristics. In total, 40 teams signed up to participate in Subtask A, and 14 of them submitted test runs. For Subtask B, 33 teams signed up to participate and 13 of them submitted runs. We present and analyze all submissions in this paper.
ALT Submission for OSACT Shared Task on Offensive Language Detection
Authors: Sabit Hassan, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Ammar Rashed, Shammur Absar Chowdhury
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Hate Speech Detection

In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51{\%} on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63{\%} on the test set.
ASU\_OPTO at OSACT4 - Offensive Language Detection for Arabic text
Authors: Amr Keleg, Samhaa R. El-Beltagy, Mahmoud Khalil
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In the past years, toxic comments and offensive speech are polluting the internet and manual inspection of these comments is becoming a tiresome task to manage. Having a machine learning based model that is able to filter offensive Arabic content is of high need nowadays. In this paper, we describe the model that was submitted to the Shared Task on Offensive Language Detection that is organized by (The 4th Workshop on Open-Source Arabic Corpora and Processing Tools). Our model makes use transformer based model (BERT) to detect offensive content. We came in the fourth place in subtask A (detecting Offensive Speech) and in the third place in subtask B (detecting Hate Speech).
OSACT4 Shared Tasks: Ensembled Stacked Classification for Offensive and Hate Speech in Arabic Tweets
Authors: Hafiz Hassaan Saeed, Toon Calders, Faisal Kamiran
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Hate Speech Detection, Word Embeddings

In this paper, we describe our submission for the OCAST4 2020 shared tasks on offensive language and hate speech detection in the Arabic language. Our solution builds upon combining a number of deep learning models using pre-trained word vectors. To improve the word representation and increase word coverage, we compare a number of existing pre-trained word embeddings and finally concatenate the two empirically best among them. To avoid under- as well as over-fitting, we train each deep model multiple times, and we include the optimization of the decision threshold into the training process. The predictions of the resulting models are then combined into a tuned ensemble by stacking a classifier on top of the predictions by these base models. We name our approach {``}ESOTP{''} (Ensembled Stacking classifier over Optimized Thresholded Predictions of multiple deep models). The resulting ESOTP-based system ranked 6th out of 35 on the shared task of Offensive Language detection (sub-task A) and 5th out of 30 on Hate Speech Detection (sub-task B).
Arabic Offensive Language Detection with Attention-based Deep Neural Networks
Authors: Bushr Haddad, Zoher Orabe, Anas Al-Abood, Nada Ghneim
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abusive Language, BIG-bench Machine Learning, Hate Speech Detection

In this paper, we tackle the problem of offensive language and hate speech detection. We proposed our methods for data preprocessing and balancing, and then we presented our Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (GRU) models used. After that, we augmented these models with attention layer. The best results achieved was using the Bidirectional Gated Recurrent Unit augmented with attention layer (Bi-GRU{\_}ATT). Keywords: Abusive Language, Text Mining, Arabic Language, Social Media Mining, Deep Learning, Convolutional Neural Network, Gated Recurrent Unit, Attention Mechanism, Machine Learning.
Offensive language detection in Arabic using ULMFiT
Authors: Mohamed Abdellatif, Ahmed Elgammal
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Language Modeling, Language Modelling, text-classification, Text Classification, Transfer Learning

In this paper, we approach the shared task OffenseEval 2020 by Mubarak et al. (2020) using ULMFiT Howard and Ruder (2018) pre-trained on Arabic Wikipedia Khooli (2019) which we use as a starting point and use the target data-set to fine-tune it. The data set of the task is highly imbalanced. We train forward and backward models and ensemble the results. We report confusion matrix, accuracy, precision, recall and F1 of the development set and report summarized results of the test set. Transfer learning method using ULMFiT shows potential for Arabic text classification. Mubarak, K. Darwish,W. Magdy, T. Elsayed, and H. Al-Khalifa. Overview of osact4 arabic offensive language detection shared task. 4, 2020. Howard and S. Ruder. Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018. Khooli. Applied data science. https://github.com/abedkhooli/ds2, 2019.
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection
Authors: Ibrahim Abu Farha, Walid Magdy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Hate Speech Detection, Transfer Learning

Offensive language and hate-speech are phenomena that spread with the rising popularity of social media. Detecting such content is crucial for understanding and predicting conflicts, understanding polarisation among communities and providing means and tools to filter or block inappropriate content. This paper describes the SMASH team submission to OSACT4{'}s shared task on hate-speech and offensive language detection, where we explore different approaches to perform these tasks. The experiments cover a variety of approaches that include deep learning, transfer learning and multitask learning. We also explore the utilisation of sentiment information to perform the previous task. Our best model is a multitask learning architecture, based on CNN-BiLSTM, that was trained to detect hate-speech and offensive language and predict sentiment.
Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic
Authors: Abdullah I. Alharbi, Mark Lee
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

Twitter and other social media platforms offer users the chance to share their ideas via short posts. While the easy exchange of ideas has value, these microblogs can be leveraged by people who want to share hatred. and such individuals can share negative views about an individual, race, or group with millions of people at the click of a button. There is thus an urgent need to establish a method that can automatically identify hate speech and offensive language. To contribute to this development, during the OSACT4 workshop, a shared task was undertaken to detect offensive language in Arabic. A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem. In addition, the use of different dialects in Arabic exacerbates this problem. To deal with the issues associated with OOV, we generated a character-level embeddings model, which was trained on a massive data collected carefully. This level of embeddings can work effectively in resolving the problem of OOV words through its ability to learn the vectors of character n-grams or parts of words. The proposed systems were ranked 7th and 8th for Subtasks A and B, respectively.
Multi-Task Learning using AraBert for Offensive Language Detection
Authors: Dj, Marc ji, Fady Baly, Wissam Antoun, Hazem Hajj
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling, Multi-Task Learning

The use of social media platforms has become more prevalent, which has provided tremendous opportunities for people to connect but has also opened the door for misuse with the spread of hate speech and offensive language. This phenomenon has been driving more and more people to more extreme reactions and online aggression, sometimes causing physical harm to individuals or groups of people. There is a need to control and prevent such misuse of online social media through automatic detection of profane language. The shared task on Offensive Language Detection at the OSACT4 has aimed at achieving state of art profane language detection methods for Arabic social media. Our team {``}BERTologists{''} tackled this problem by leveraging state of the art pretrained Arabic language model, AraBERT, that we augment with the addition of Multi-task learning to enable our model to learn efficiently from little data. Our Multitask AraBERT approach achieved the second place in both subtasks A {\&} B, which shows that the model performs consistently across different tasks.