pwc-archive/papers-with-abstracts huggingface dataset:
Intuitive and Formal Transparency in Annotation Schemes
Authors: Harry Bunt
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Relation

This paper explores the application of the notion of ‘transparency’ to annotation schemes, understood as the properties that make it easy for potential users to see the scope of the scheme, the main concepts used in annotations, and the ways these concepts are interrelated. Based on an analysis of annotation schemes in the ISO Semantic Annotation Framework, it is argued that the way these schemes make use of ‘metamodels’ is not optimal, since these models are often not entirely clear and not directly related to the formal specification of the scheme. It is shown that by formalizing the relation between metamodels and annotations, by formalizing the relation between metamodels and annotations, both can benefit and can be made simpler, and the annotation scheme becomes intuitively more transparent.
Towards Practical Semantic Interoperability in NLP Platforms
Authors: Julian Moreno-Schneider, Rémi Calizzano, Florian Kintzel, Georg Rehm, Dimitris Galanis, Ian Roberts
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Translation

Interoperability is a necessity for the resolution of complex tasks that require the interconnection of several NLP services. This article presents the approaches that were adopted in three scenarios to address the respective interoperability issues. The first scenario describes the creation of a common REST API for a specific platform, the second scenario presents the interconnection of several platforms via mapping of different representation formats and the third scenario shows the complexities of interoperability through semantic schema mapping or automatic translation.
Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset
Authors: Kana Koyano, Hitomi Yanaka, Koji Mineshima, Daisuke Bekki
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

Numeral expressions in Japanese are characterized by the flexibility of quantifier positions and the variety of numeral suffixes. However, little work has been done to build annotated corpora focusing on these features and datasets for testing the understanding of Japanese numeral expressions. In this study, we build a corpus that annotates each numeral expression in an existing phrase structure-based Japanese treebank with its usage and numeral suffix types. We also construct an inference test set for numerical expressions based on this annotated corpus. In this test set, we particularly pay attention to inferences where the correct label differs between logical entailment and implicature and those contexts such as negations and conditionals where the entailment labels can be reversed. The baseline experiment with Japanese BERT models shows that our inference test set poses challenges for inference involving various types of numeral expressions.
Annotating complex words to investigate the semantics of derivational processes
Authors: Rossella Varvara, Justine Salvadori, Richard Huyghe
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6
Tasks: Descriptive

In this paper, we present and test an annotation scheme designed to analyse the semantic properties of derived nouns in context. Aiming at a general semantic comparison of morphological processes, we use a descriptive model that seeks to capture semantic regularities among lexemes and affixes, rather than match occurrences to word sense inventories. We annotate two distinct features of target words: the ontological type of the entity they denote and their semantic relationship with the word they derive from. As illustrated through an annotation experiment on French corpus data, this procedure allows us to highlight semantic differences and similarities between affixes by investigating the number and frequency of their semantic functions, as well as the relation between affix polyfunctionality and lexical ambiguity.
Annotating Propositional Attitude Verbs and their Arguments
Authors: Marta Ricchiardi, Elisabetta Jezek
Date: 2022-06-01 00:00:00
Proceeding: ISA (LREC) 2022 6

This paper describes the results of an empirical study on attitude verbs and propositional attitude reports in Italian. Within the framework of a project aiming at acquiring argument structures for Italian verbs from corpora, we carried out a systematic annotation that aims at individuating which verbs are actually attitude verbs in Italian. The result is a list of 179 argument structures based on corpus-derived pattern of use for 126 verbs that behave as attitude verbs. The distribution of these verbs in the corpus suggests that not only the canonical that-clauses, i.e. subordinates introduced by the complementizerte che, but also direct speech, infinitives introduced by the complementizer di, and some nominals are good candidates to express propositional contents in propositional attitude reports. The annotation also enlightens some issues between semantics and ontology, concerning the relation between events and propositions.
NewYeS: A Corpus of New Year’s Speeches with a Comparative Analysis
Authors: Anna Tramarin, Carlo Strapparava
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Sentiment Analysis

This paper introduces the NewYeS corpus, which contains the Christmas messages and New Year’s speeches held at the end of the year by the heads of state of different European countries (namely Denmark, France, Italy, Norway, Spain and the United Kingdom). The corpus was collected via web scraping of the speech transcripts available online. A comparative analysis was conducted to examine some of the cultural differences showing through the texts, namely a frequency distribution analysis of the term “God” and the identification of the three most frequent content words per year, with a focus on years in which significant historical events happened. An analysis of positive and negative emotion scores, examined along with the frequency of religious references, was carried out for those countries whose languages are supported by LIWC, a tool for sentiment analysis. The corpus is available for further analyses, both comparative (across countries) and diachronic (over the years).
Correlating Political Party Names in Tweets, Newspapers and Election Results
Authors: Eric Sanders, Antal Van den Bosch
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6

Twitter has been used as a textual resource to attempt to predict the outcome of elections for over a decade. A body of literature suggests that this is not consistently possible. In this paper we test the hypothesis that mentions of political parties in tweets are better correlated with the appearance of party names in newspapers than to the intention of the tweeter to vote for that party. Five Dutch national elections are used in this study. We find only a small positive, negligible difference in Pearson’s correlation coefficient as well as in the absolute error of the relation between tweets and news, and between tweets and elections. However, we find a larger correlation and a smaller absolute error between party mentions in newspapers and the outcome of the elections in four of the five elections. This suggests that newspapers are a better starting point for predicting the election outcome than tweets.
Debating Europe: A Multilingual Multi-Target Stance Classification Dataset of Online Debates
Authors: Valentin Barriere, Alexandra Balahur, Brian Ravenet
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Stance Classification, XLM-R

We present a new dataset of online debates in English, annotated with stance. The dataset was scraped from the “Debating Europe” platform, where users exchange opinions over different subjects related to the European Union. The dataset is composed of 2600 comments pertaining to 18 debates related to the “European Green Deal”, in a conversational setting. After presenting the dataset and the annotated sub-part, we pre-train a model for a multilingual stance classification over the X-stance dataset before fine-tuning it over our dataset, and vice-versa. The fine-tuned models are shown to improve stance classification performance on each of the datasets, even though they have different languages, topics and targets. Subsequently, we propose to enhance the performances over “Debating Europe” with an interaction-aware model, taking advantage of the online debate structure of the platform. We also propose a semi-supervised self-training method to take advantage of the imbalanced and unlabeled data from the whole website, leading to a final improvement of accuracy by 3.4% over a Vanilla XLM-R model.
An Unsupervised Approach to Discover Media Frames
Authors: Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Articles, Community Detection

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
Electoral Agitation Dataset: The Use Case of the Polish Election
Authors: Mateusz Baran, Mateusz Wójcik, Piotr Kolebski, Michał Bernaczyk, Krzysztof Rajda, Lukasz Augustyniak, Tomasz Kajdanowicz
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Language Modeling, Language Modelling

The popularity of social media makes politicians use it for political advertisement. Therefore, social media is full of electoral agitation (electioneering), especially during the election campaigns. The election administration cannot track the spread and quantity of messages that count as agitation under the election code. It addresses a crucial problem, while also uncovering a niche that has not been effectively targeted so far. Hence, we present the first publicly open data set for detecting electoral agitation in the Polish language. It contains 6,112 human-annotated tweets tagged with four legally conditioned categories. We achieved a 0.66 inter-annotator agreement (Cohen’s kappa score). An additional annotator resolved the mismatches between the first two improving the consistency and complexity of the annotation process. The newly created data set was used to fine-tune a Polish Language Model called HerBERT (achieving a 68% F1 score). We also present a number of potential use cases for such data sets and models, enriching the paper with an analysis of the Polish 2020 Presidential Election on Twitter.
Enhancing Geocoding of Adjectival Toponyms With Heuristics
Authors: Breno Dourado Sá, Ticiana Coelho da Silva, Jose Antonio Fernandes de Macedo
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6

Unstructured text documents such as news and blogs often present references to places. Those references, called toponyms, can be used in various applications like disaster warning and touristic planning. However, obtaining the correct coordinates for toponyms, called geocoding, is not easy since it’s common for places to have the same name as other locations. The process becomes even more challenging when toponyms appear in adjectival form, as they are different from the place’s actual name. This paper addresses the geocoding task and aims to improve, through a heuristic approach, the process for adjectival toponyms. So first, a baseline geocoder is defined through experimenting with a set of heuristics. After that, the baseline is enhanced by adding a normalization step to map adjectival toponyms to their noun form at the beginning of the geocoding process. The results show improved performance for the enhanced geocoder compared to the baseline and other geocoders.
Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish
Authors: Luise Dürlich, Sebastian Reimann, Gustav Finnveden, Joakim Nivre, Sara Stymne
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Sentence

Causality detection is the task of extracting information about causal relations from text. It is an important task for different types of document analysis, including political impact assessment. We present two new data sets for causality detection in Swedish. The first data set is annotated with binary relevance judgments, indicating whether a sentence contains causality information or not. In the second data set, sentence pairs are ranked for relevance with respect to a causality query, containing a specific hypothesized cause and/or effect. Both data sets are carefully curated and mainly intended for use as test data. We describe the data sets and their annotation, including detailed annotation guidelines. In addition, we present pilot experiments on cross-lingual zero-shot and few-shot causality detection, using training data from English and German.
Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
Authors: Evan Dufraisse, Célina Treuillier, Armelle Brun, Julien Tourille, Sylvain Castagnos, Adrian Popescu
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Diversity, News Recommendation, Opinion Mining, Recommendation Systems

Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.
Creation of Polish Online News Corpus for Political Polarization Studies
Authors: Joanna Szwoch, Mateusz Staszkow, Rafal Rzepka, Kenji Araki
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6
Tasks: Articles

In this paper we describe a Polish news corpus as an attempt to create a filtered, organized and representative set of texts coming from contemporary online press articles from two major Polish TV news providers: commercial TVN24 and state-owned TVP Info. The process consists of web scraping, data cleaning and formatting. A random sample was selected from prepared data to perform a classification task. The random forest achieved the best prediction results out of all considered models. We believe that this dataset is a valuable contribution to existing Polish language corpora as online news are considered to be formal and relatively mistake-free, therefore, a reliable source of correct written language, unlike other online platforms such as blogs or social media. Furthermore, to our knowledge, such corpus from this period of time has not been created before. In the future we would like to expand this dataset with articles coming from other online news providers, repeat the classification task on a bigger scale, utilizing other algorithms. Our data analysis outcomes might be a relevant basis to improve research on a political polarization and propaganda techniques in media.
Annotation of expressive dimensions on a multimodal French corpus of political interviews
Authors: Jules Cauzinille, Marc Evrard, Nikita Kiselov, Albert Rilliard
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6

We present a French corpus of political interviews labeled at the utterance level according to expressive dimensions such as Arousal. This corpus consists of 7.5 hours of high-quality audio-visual recordings with transcription. At the time of this publication, 1 hour of speech was segmented into short utterances, each manually annotated in Arousal. Our segmentation approach differs from similar corpora and allows us to perform an automatic Arousal prediction baseline by building a speech-based classification model. Although this paper focuses on the acoustic expression of Arousal, it paves the way for future work on conflictual and hostile expression recognition as well as multimodal architectures.
TransCasm: A Bilingual Corpus of Sarcastic Tweets
Authors: Desline Simon, Sheila Castilho, Pintu Lohar, Haithem Afli
Date: 2022-06-01 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6

Sarcasm is extensively used in User Generated Content (UGC) in order to express one’s discontent, especially through blogs, forums, or social media such as Twitter. Several works have attempted to detect and analyse sarcasm in UGC. However, the lack of freely available corpora in this field makes the task even more difficult. In this work, we present “TransCasm” corpus, a parallel corpus of sarcastic tweets translated from English into French along with their non-sarcastic representations. To build the bilingual corpus of sarcasm, we select the “SIGN” corpus, a monolingual data set of sarcastic tweets and their non-sarcastic interpretations, created by (Peled and Reichart, 2017). We propose to define linguistic guidelines for developing “TransCasm” which is the first ever bilingual corpus of sarcastic tweets. In addition, we utilise “TransCasm” for building a binary sarcasm classifier in order to identify whether a tweet is sarcastic or not. Our experiment reveals that the sarcasm classifier achieves 61% accuracy on detecting sarcasm in tweets. “TransCasm” is now freely available online and is ready to be explored for further research.
Lexicon-driven approach for Terminology: specialized resources on the environment in Brazilian Portuguese
Authors: Flávia Lamberti Arraes
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6

This paper presents a terminological research carried out to account for terms of the environment in Brazilian Portuguese based on a lexico-semantic perspective for Terminology (L’Homme, 2015, 2016, 2017, 2020; L’Homme et al., 2014, 2020). This work takes place in the context of a collaboration for the development of DiCoEnviro (Dictionnaire Fondamental de l’Environnment – Fundamental Dictionary on the environment), a multilingual terminological resource developed by the Observatoire de Linguistique Sens Texte at the University of Montreal, Canada. By following a methodolgy especially devised to develop terminological work based on a lexicon-driven approach (L’Homme et al., 2020), the terminological analysis reveals how the linguistic behavior of terms may be unveiled and how this is effective for identifying the meaning of a term and supporting meaning distinctions.
Knowledge Representation and Language Simplification of Human Rights
Authors: Sara Silecchia, Federica Vezzani, Giorgio Maria Di Nunzio
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6
Tasks: Humanitarian

In this paper, we propose the description of a very recent interdisciplinary project aiming at analysing both the conceptual and linguistic dimensions of humanitarian rights terminology. This analysis will result in the form of a new knowledge-based multilingual terminological resource which is designed in order to meet the FAIR principles for Open Science and will serve, in the future, as a prototype for the development of a new software for the simplified rewriting of international legal texts relating to human rights. Given the early stage of the project, we will focus on the description of its rationale, the planned workflow, and the theoretical approach which will be adopted to achieve the main goal of this ambitious research project.
Converting from the Nordic Terminological Record Format to the TBX Format
Authors: Maria Skeppstedt, Marie Mattson, Magnus Ahltorp, Rickard Domeij
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6

Rikstermbanken (Sweden’s National Term Bank), which was launched in 2009, uses the Nordic Terminological Record Format (NTRF) for organising its terminological data. Since then, new terminology formats have been established as standards, e.g., the Termbase eXchange format (TBX). We here describe work carried out by the Institute for Language and Folklore within the Federated eTranslation TermBank Network Action. This network develops a technical infrastructure for facilitating sharing of terminology resources throughout Europe. To be able to share some of the term collections of Rikstermbanken within this network and export them to Eurotermbank, we have implemented a conversion from the Nordic Terminological Record Format, as used in Rikstermbanken, to the TBX format.
A Dataset for Term Extraction in Hindi
Authors: Shubhanker Banerjee, Bharathi Raja Chakravarthi, John Philip McCrae
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6
Tasks: Machine Translation, Term Extraction

Automatic Term Extraction (ATE) is one of the core problems in natural language processing and forms a key component of text mining pipelines of domain specific corpora. Complex low-level tasks such as machine translation and summarization for domain specific texts necessitate the use of term extraction systems. However, the development of these systems requires the use of large annotated datasets and thus there has been little progress made on this front for under-resourced languages. As a part of ongoing research, we present a dataset for term extraction from Hindi texts in this paper. To the best of our knowledge, this is the first dataset that provides term annotated documents for Hindi. Furthermore, we have evaluated this dataset on statistical term extraction methods and the results obtained indicate the problems associated with development of term extractors for under-resourced languages.
Terminology extraction using co-occurrence patterns as predictors of semantic relevance
Authors: Rogelio Nazar, David Lindemann
Date: 2022-06-01 00:00:00
Proceeding: TERM (LREC) 2022 6
Tasks: Term Extraction

We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order and at variable distances. In this way, term candidates are ranked higher if they show a tendency to co-occur with a selected group of other units, as opposed to those showing more uniform distributions. No external resources are needed for the application of the method, but performance improves when provided with a pre-existing term list. We present results of the application of this method to a Spanish-English Linguistics corpus, and the evaluation compares favourably with a standard method based on reference corpora.