pwc-archive/papers-with-abstracts huggingface dataset:
Aligning the Bulgarian BTB WordNet with the Bulgarian Wikipedia
Authors: Kiril Simov, Petya Osenova, Laska Laskova, Ivajlo Radev, Zara Kancheva
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Articles

The paper reports on an ongoing work that manually maps the Bulgarian WordNet BTB-WN with Bulgarian Wikipedia. The preparatory work of extracting the Wikipedia articles and provisionally relating them to the WordNet lemmas was done automatically. The manual work includes checking of the corresponding senses in both resources as well as the missing ones. The main cases of mapping are considered. The first experiments of mapping about 1000 synsets show the establishment of more than 78 % of exact correspondences and nearly 15 % of new synsets.
English-Turkish Parallel Semantic Annotation of Penn-Treebank
Authors: Bilge Nas Arıcan, Özge Bakay, Begüm Avar, Olcay Taner Yildiz, Özlem Ergelen
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.
Comparing Sense Categorization Between English PropBank and English WordNet
Authors: Özge Bakay, Begüm Avar, Olcay Taner Yildiz
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.
Building ASLNet, a Wordnet for American Sign Language
Authors: Colin Lualdi, Jack Hudson, Christiane Fellbaum, Noah Buchholz
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

We discuss the creation of ASLNet by aligning the Princeton WordNet (PWN) with SignStudy, an online database of American Sign Language (ASL) signs. This alignment will have many immediate benefits for first and second-sign language learners as well as ASL researchers by highlighting semantic relations among signs. We begin to address the interesting theoretical question of to what extent the wordnet-style organization of the English lexicon (and those of wordnets in other spoken languages) is applicable to ASL, and whether ASL requires positing additional, language or modality-specific relations among signs. Significantly, the mapping of SignStudy and PWN provides a bridge between ASL and the worldwide wordnet community, which comprises speakers of dozens of languages working in academic and language technology settings.
A collaborative system for building and maintaining wordnets.
Authors: Tomasz Naskręt
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

A collaborative system for wordnet construction and maintenance is presented. Its key modules include WordnetLoom editor, Wordnet Tracker and JavaScript Graph. They offer a number of functionalities that allow solving problems on every stage of building, editing and aligning wordnets by teams of lexicographers working in parallel. The experience collected in recent years has allowed us to refine applications and add new modules to provide the best user experience in a reliable and easily maintainable way.
Enriching Keywords Database UsingWordnets – a Case Study
Authors: Tomasz Jastrząb, Grzegorz Kwiatkowski
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Classification

In the paper, we study the case of building a keywords database related to the Polish Classification of Activities (PKD 2007). The database enables automatic classification of the companies to the industry branches. The classification is performed based on the company’s activity description. We present the initial design of the keywords database and the ways in which wordnets were used to enrich it. Finally, we present the preliminary statistical evaluation of the produced resource.
Propagation of emotions, arousal and polarity in WordNet using Heterogeneous Structured Synset Embeddings
Authors: Jan Kocoń, Arkadiusz Janz
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: regression

In this paper we present a novel method for emotive propagation in a wordnet based on a large emotive seed. We introduce a sense-level emotive lexicon annotated with polarity, arousal and emotions. The data were annotated as a part of a large study involving over 20,000 participants. A total of 30,000 lexical units in Polish WordNet were described with metadata, each unit received about 50 annotations concerning polarity, arousal and 8 basic emotions, marked on a multilevel scale. We present a preliminary approach to propagating emotive metadata to unlabeled lexical units based on the distribution of manual annotations using logistic regression and description of mixed synset embeddings based on our Heterogeneous Structured Synset Embeddings.
Testing Zipf’s meaning-frequency law with wordnets as sense inventories
Authors: Francis Bond, Arkadiusz Janz, Marek Maziarz, Ewa Rudnicka
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: LEMMA

According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, French, Polish, Japanese, Indonesian and Chinese. We have proved that the law works pretty well for all of these languages if we take - as Zipf did - mean values of meaning count and averaged ranks. On the other hand, the law disastrously fails in predicting the number of senses for a single lemma. We have also provided the evidence that slope coefficients of Zipfian log-log linear model may vary from language to language.
plWordNet 4.1 - a Linguistically Motivated, Corpus-based Bilingual Resource
Authors: Agnieszka Dziob, Maciej Piasecki, Ewa Rudnicka
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

The paper presents the latest release of the Polish WordNet, namely plWordNet 4.1. The most significant developments since 3.0 version include new relations for nouns and verbs, mapping semantic role-relations from the valency lexicon Walenty onto the plWordNet structure and sense-level inter-lingual mapping. Several statistics are presented in order to illustrate the development and contemporary state of the wordnet.
A Comparison of Sense-level Sentiment Scores
Authors: Francis Bond, Arkadiusz Janz, Maciej Piasecki
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

In this paper, we compare a variety of sense-tagged sentiment resources, including SentiWordNet, ML-Senticon, plWordNet emo and the NTU Multilingual Corpus. The goal is to investigate the quality of the resources and see how well the sentiment polarity annotation maps across languages.
Portuguese Manners of Speaking
Authors: Valeria de Paiva, Alexandre Rademaker
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

Lexical resources need to be as complete as possible. Very little work seems to have been done on adverbs, the smallest part of speech class in Princeton WordNet counting the number of synsets. Amongst adverbs, manner adverbs ending in ‘-ly’ seem the easiest to work with, as their meaning is almost the same as the one of the associated adjective. This phenomenon seems to be parallel in English and Portuguese, where these manner adverbs finish in the suffix ‘-mente’. We use this correspondence to improve the coverage of adverbs in the lexical resource OpenWordNet-PT, a wordnet for Portuguese.
Completing the Princeton Annotated Gloss Corpus Project
Authors: Alexandre Rademaker, Bruno Cuconato, Alessandra Cid, Alexandre Tessarollo, Henrique Andrade
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

In the Princeton WordNet Gloss Corpus, the word forms from the definitions (“glosses”) in WordNet’s synsets are manually linked to the context-appropriate sense in the WordNet. The glosses then become a sense-disambiguated corpus annotated against WordNet version 3.0. The result is also called a semantic concordance, which can be seen as both a lexicon (WordNet extension) and an annotated corpus. In this work we motivate and present the initial steps to complete the annotation of all open-class words in this corpus. Finally, we introduce a freely-available annotation interface built as an Emacs extension, and evaluate a preliminary annotation effort.
GeoNames Wordnet (geown): extracting wordnets from GeoNames
Authors: Francis Bond, Arthur Bond
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper introduces a new multilingual lexicon of geographical place names. The names are based on (and linked to) the GeoNames collection. Each location is treated as a new synset, which is linked by instance_hypernym to a small set of supertypes. These supertypes are linked to the collaborative interlingual index, based on mappings from GeoDomainWordnet. If a location is already in the interlingual index, then it is also linked to the entry, using mappings from the Geo-Wordnet. Finally, if GeoNames places the location in a larger location, this is linked using the mero_location link. Wordnets can be built for any language in GeoNames, we give results for those wordnets in the Open Multilingual Wordnet. We discuss how it is mapped and the characteristics of the extracted wordnets.
New Polysemy Structures in Wordnets Induced by Vertical Polysemy
Authors: Ahti Lohk, Heili Orav, Kadri Vare, Francis Bond, Rasmus Vaik
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper aims to study auto-hyponymy and auto-troponymy relations (or vertical polysemy) in 11 wordnets uploaded into the new Open Multilingual Wordnet (OMW) webpage. We investigate how vertical polysemy forms polysemy structures (or sense clusters) in semantic hierarchies of the wordnets. Our main results and discoveries are new polysemy structures that have not previously been associated with vertical polysemy, along with some inconsistencies of semantic relations analysis in the studied wordnets, which should not be there. In the case study, we turn attention to polysemy structures in the Estonian Wordnet (version 2.2.0), analyzing them and giving the lexicographers comments. In addition, we describe the detection algorithm of polysemy structures and an overview of the state of polysemy structures in 11 wordnets.
Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting
Authors: Christian Huber, Juan Hussain, Tuan-Nam Nguyen, Kaihang Song, Sebastian Stüker, Alexander Waibel
Date: 2020-12-01 00:00:00
Proceeding: AACL (lifelongnlp) 2020 12
Tasks: Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition

When training speech recognition systems, one often faces the situation that sufficient amounts of training data for the language in question are available but only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data. In this paper we present experiments in adapting end-to-end speech recognition systems by a method which is called batch-weighting and which we contrast against regular fine-tuning, i.e., to continue to train existing neural speech recognition models on adaptation data. We perform experiments using theses techniques in adapting to topic, accent and vocabulary, showing that batch-weighting consistently outperforms fine-tuning. In order to show the generalization capabilities of batch-weighting we perform experiments in several languages, i.e., Arabic, English and German. Due to its relatively small computational requirements batch-weighting is a suitable technique for supervised life-long learning during the life-time of a speech recognition system, e.g., from user corrections.
Inferring Neuroticism of Twitter Users by Utilizing their Following Interests
Authors: Joran Cornelisse
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12

Twitter is a medium where, when used adequately, users’ interests can be derived from what he follows. This characteristic can make it attractive for a source of personality derivation. We set out to test the hypothesis that, analogous to the Lexical hypothesis, which posits that word use should reveal personality, following behavior on social media should reveal personality aspects. We used a two-step approach, wherein the first stage, we selected accounts for whom it was possible to infer personality profiles to some extent using available literature on personality and interests. On these accounts, we trained a regression model and segmented the derived features using hierarchical cluster analysis. In the second stage, we obtained a small sample of users’ personalities via a questionnaire and tested whether the model from stage 1 correlated with the users from step 2. The the explained variance for the neurotic and neutral neuroticism groups indicated significant results (R2 = .131, p = .0205; R2 = .22, p = .0044). Confirming the hypothesis that following behavior should be correlated with one’s interests and that interests are correlated with the neuroticism personality dimension.
Red Is Open-Minded, Blue Is Conscientious: Predicting User Traits From Instagram Image Data
Authors: Lisa Branz, Patricia Brockmann, Annika Hinze
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: regression

Various studies have addressed the connection between a user’s traits and their social media content. This paper explores the relationship between gender, age and Big Five personality traits of 179 university students from Germany and their Instagram images. With regards to both image features and image content, significant differences between genders as well as preferences related to age and personality traits emerged. Gender, age and personality traits are predicted using machine learning classification and regression methods. This work is the first of its kind to focus on data from European Instagram users, as well as to predict age from Instagram image features and content on a fine-grained level.
Persuasiveness of News Editorials depending on Ideology and Personality
Authors: Roxanne El Baff, Khalid Al Khatib, Benno Stein, Henning Wachsmuth
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Persuasiveness

News editorials aim to shape the opinions of their readership and the general public on timely controversial issues. The impact of an editorial on the reader’s opinion does not only depend on its content and style, but also on the reader’s profile. Previous work has studied the effect of editorial style depending on general political ideologies (liberals vs.conservatives). In our work, we dig deeper into the persuasiveness of both content and style, exploring the role of the intensity of an ideology (lean vs.extreme) and the reader’s personality traits (agreeableness, conscientiousness, extraversion, neuroticism, and openness). Concretely, we train content- and style-based models on New York Times editorials for different ideology- and personality-specific groups. Our results suggest that particularly readers with extreme ideology and non “role model” personalities are impacted by style. We further analyze the importance of various text features with respect to the editorials’ impact, the readers’ profile, and the editorials’ geographical scope.
KanCMD: Kannada CodeMixed Dataset for Sentiment Analysis and Offensive Language Detection
Authors: Adeep Hande, Ruba Priyadharshini, Bharathi Raja Chakravarthi
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Language Identification, Multi-Task Learning, Sentiment Analysis

We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations
Authors: Jonggu Kim, Hyeonmok Ko, Seoha Song, Saebom Jang, Jiyeon Hong
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Emotion Recognition, Language Modeling, Language Modelling

Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing tasks. That is because it is helpful to use individual contextualized representations of self-attention layers as to initialize parameters for downstream tasks. Yet, unfortunately, use of pretrained language models for emotion recognition in conversations has not been studied enough. We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations. Furthermore, we propose contextual augmentation of pretrained language models for emotion recognition in conversations, which is to consider not only previous utterances, but also conversation-related information such as speakers, speech acts and topics. We classify information based on what the information is related to, and propose position of words corresponding to the information in the entire input sequence. To validate the proposed method, we conduct experiments on the DailyDialog dataset which contains abundant annotated information of conversations. The experiments show that the proposed method achieves state-of-the-art F1 scores on the dataset and significantly improves the performance.
Social Media Unrest Prediction during the COVID-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises
Authors: Dirk Johannßen, Chris Biemann
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12

The COVID-19 pandemic has caused international social tension and unrest. Besides the crisis itself, there are growing signs of rising conflict potential of societies around the world. Indicators of global mood changes are hard to detect and direct questionnaires suffer from social desirability biases. However, so-called implicit methods can reveal humans intrinsic desires from e.g. social media texts. We present psychologically validated social unrest predictors and replicate scalable and automated predictions, setting a new state of the art on a recent German shared task dataset. We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic by comparing established psychological predictors on samples of tweets from spring 2019 with spring 2020. The results show a significant increase of the conflict indicating psychometrics. With this work, we demonstrate the applicability of automated NLP-based approaches to quantitative psychological research.
Topic and Emotion Development among Dutch COVID-19 Twitter Communities in the early Pandemic
Authors: Boris Marinov, Jennifer Spenader, Tommaso Caselli
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12

The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major user communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the national focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific group of users.
Sentiments in Russian Medical Professional Discourse during the Covid-19 Pandemic
Authors: Irina Ovchinnikova, Liana Ermakova, Diana Nurbakova
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Sentiment Analysis

Medical discourse within the professional community has undeservingly received very sparse researchers’ attention. Medical professional discourse exists offline and online. We carried out sentiment analysis on titles and text descriptions of materials published on the Russian portal Mir Vracha (90,000 word forms approximately). The texts were generated by and for physicians. The materials include personal narratives describing participants’ professional experience, participants’ opinions about pandemic news and events in the professional sphere, and Russian reviews and discussion of papers published in international journals in English. We present the first results and discussion of the sentiment analysis of Russian online medical discourse. Based on the results of sentiment analysis and discourse analysis, we described the emotions expressed in the forum and the linguistic means the forum participants used to verbalise their attitudes and emotions while discussing the Covid-19 pandemic. The results showed prevalence of neutral texts in the publications since the medical professionals are interested in research materials and outcomes. In the discussions and personal narratives, the forum participants expressed negative sentiments by colloquial words and figurative language.
Multilingual Emoticon Prediction of Tweets about COVID-19
Authors: Stefanos Stoikos, Mike Izbicki
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Prediction

Emojis are a widely used tool for encoding emotional content in informal messages such as tweets,and predicting which emoji corresponds to a piece of text can be used as a proxy for measuring the emotional content in the text. This paper presents the first model for predicting emojis in highly multilingual text.Our BERTmoticon model is a fine-tuned version of the BERT model,and it can predict emojis for text written in 102 different languages.We trained our BERTmoticon model on 54.2 million geolocated tweets sent in the first 6 months of 2020,and we apply the model to a case study analyzing the emotional reaction of Twitter users to news about the coronavirus. Example findings include a spike in sadness when the World Health Organization (WHO) declared that coronavirus was a global pandemic, and a spike in anger and disgust when the number of COVID-19 related deaths in the United States surpassed one hundred thousand. We provide an easy-to-use and open source python library for predicting emojis with BERTmoticon so that the model can easily be applied to other data mining tasks.