ymoslem/acl-abstracts huggingface dataset:
Quote Detection: A New Task and Dataset for NLP (2023)

Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We measure unique vocabulary usage by a state-of-the-art language model and perform comparative statistical analysis against the Cornell Movie-Quotes Corpus. Furthermore, we run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts.
Improving Long-Text Authorship Verification via Model Selection and Data Tuning (2023)

Authorship verification is used to link texts written by the same author without needing a model per author, making it useful to deanonymizing users spreading text with malicious intent. In this work, we evaluated our Cross-Encoder system with four Transformers using differently tuned variants of fanfiction data and found that our BigBird pipeline outperformed Longformer, RoBERTa, and ELECTRA and performed competitively against the official top ranked system from the PAN evaluation. We also examined the effect of authors and fandoms not seen in training on model performance. Through this, we found fandom has the greatest influence on true trials, and that a balanced training dataset in terms of class and fandom performed the most consistently.
Fractality of informativity in 300 years of English scientific writing (2023)

Scientific writing is assumed to have become more informationally dense over time (Halliday, 1988; Biber and Gray, 2016). By means of fractal analysis, we study whether over time the degree of informativity has become more persistent with predictable patterns of gradual changes between high vs. low informational content, indicating a trend towards an optimal code for scientific communication.
Direct Speech Quote Attribution for Dutch Literature (2023)

We present a dataset and system for quote attribution in Dutch literature. The system is implemented as a neural module in an existing NLP pipeline for Dutch literature (dutchcoref; van Cranenburgh, 2019). Our contributions are as follows. First, we provide guidelines for Dutch quote attribution and annotate 3,056 quotes in fragments of 42 Dutch literary novels, both contemporary and classic. Second, we present three neural quote attribution classifiers, optimizing for precision, recall, and F1. Third, we perform an evaluation and analysis of quote attribution performance, showing that in particular, quotes with an implicit speaker are challenging, and that such quotes are prevalent in contemporary fiction (57{\%}, compared to 32{\%} for classic novels). On the task of quote attribution, we achieve an improvement of 8.0{\%} F1 points on contemporary fiction and 1.9{\%} F1 points on classic novels. Code, data, and models are available at \url{https://github.com/anonymized/repository}.
Great Bibliographies as a Source of Data for the Humanities -- NLP in the Analysis of Gender of Book Authors in German Countries and in Poland (1801-2021) (2023)

The subject of this article is the application of NLP and text-mining methods to the analysis of two large bibliographies: Polish one, based on the catalogs of the National Library in Warsaw, and the other German one, created by Deutsche Nationalbibliothek. The data in both collections are stored in MARC 21 format, allowing the selection of relevant fields that are used for further processing (basically author, title, and date). The volume of the Polish corpus (after filtering out non-relevant or incomplete items) includes 1.4 mln of records, and that of the German corpus 7.5 mln records. The time span of both bibliographies extends from 1801 to 2021. The aim of the study is to compare the gender distribution of book authors in Polish and German databases over more than two centuries. The proportions of male and female authors since 1801 were calculated automatically, and NLP methods such as document vector embedding based on deep BERT networks were used to extract topics from titles. The gender of the Polish authors was recognized based on the morphology of the first names, and that of the German authors based on a predefined list. The study found that the proportion of female authors has been steadily increasing both in Poland and in German countries (currently around 43{\%}). However, the topics of women`s and men`s writings invariably remain different since 1801.
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation (2023)

Emotion regulation is a crucial element in dealing with emotional events and has positive effects on mental health. This paper aims to provide a more comprehensive understanding of emotional events by introducing a new French corpus of emotional narratives collected using a questionnaire for emotion regulation. We follow the theoretical framework of the Component Process Model which considers emotions as dynamic processes composed of four interrelated components (behavior, feeling, thinking and territory). Each narrative is related to a discrete emotion and is structured based on all emotion components by the writers. We study the interaction of components and their impact on emotion classification with machine learning methods and pre-trained language models. Our results show that each component improves prediction performance, and that the best results are achieved by jointly considering all components. Our results also show the effectiveness of pre-trained language models in predicting discrete emotion from certain components, which reveal differences in how emotion components are expressed.
Linking the Neulateinische Wortliste to the LiLa Knowledge Base of Interoperable Resources for Latin (2023)

This paper describes the process of interlinking a lexical resource consisting of a list of more than 20,000 Neo-Latin words with other resources for Latin. The resources are made interoperable thanks to their linking to the anonymous Knowledge Base, which applies Linguistic Linked Open Data practices and data categories to describe and publish on the Web both textual and lexical resources for the Latin language.
What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models (2023)

Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64{\%} with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.
Constructing a Credible Estimation for Overreporting of Climate Adaptation Funds in the Creditor Reporting System (2023)

Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. How ever, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. Research has questioned the credibility of reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as {\textquotedblleft}overreported{\textquotedblright}. To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of 89.81{\%}{\ensuremath{\pm}}0.83{\%} (tenfold cross-validation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes' theorem. This enables a comparison of different annotation schemes to estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of 32.03{\%} with a credible interval of [19.81{\%}; 48.34{\%}].
\textquotedblleftWho is the Madonna of Italian-American Literature?\textquotedblright: Target Entity Extraction and Analysis of Vossian Antonomasia (2023)

In this paper, we present approaches for the automated extraction and disambiguation of a part of the stylistic device Vossian Antonomasia (VA), namely the target entity that is described by the expression. We model the problem as a coreference resolution task and a question answering task and also combine both tasks. To tackle these tasks, we utilize state-of-the-art models in these areas. In addition, we visualize the connection between the source and target entities of VA in a web demo to get a deeper understanding of the interaction of entities used in VA expressions.
Detecting intersectionality in NER models: A data-driven approach (2023)

The presence of bias is a pressing concern for both engineers and users of language technology. What is less clear is how exactly bias can be measured, so as to rank models relative to the biases they display. Using an innovative experimental method involving data augmentation, we measure the effect of intersectional biases in Danish models used for Name Entity Recognition (NER). We quantify differences in representational biases, understood as a systematic difference in error or what is called error disparity. Our analysis includes both gender and ethnicity to illustrate the effect of multiple dimensions of bias, as well as experiments which look to move beyond a narrowly binary analysis of gender. We show that all contemporary Danish NER models perform systematically worse on non-binary and minority ethnic names, while not showing significant differences for typically Danish names. Our data augmentation technique can be applied on other languages to test for biases which might be relevant for researchers applying NER models to the study of cultural heritage data.
OdyCy -- A general-purpose NLP pipeline for Ancient Greek (2023)

This paper presents a general-purpose NLP pipeline that achieves state-of-the-art performance on the Ancient Greek Perseus UD Treebank for several tasks (POS Tagging, Morphological Analysis and Dependency Parsing), and close to state-of-the-art performance on the Proiel UD Treebank. Our aim is to provide a reproducible, open source language processing pipeline for Ancient Greek, capable of handling input texts of varying quality. We measure the performance of our model against other comparable tools and then evaluate lemmatization errors.
Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities (2023)

Olfaction is a rather understudied sense compared to the other senses. In NLP, however, there have been recent attempts to develop taxonomies and benchmarks specifically designed to capture smell-related information. In this work, we further extend this research line by presenting a supervised system for olfactory information extraction in English. We cast this problem as a token classification task and build a system that identifies smell words, smell sources and qualities. The classifier is then applied to a set of English historical corpora, covering different domains and written in a time period between the 15th and the 20th Century. A qualitative analysis of the extracted data shows that they can be used to infer interesting information about smelly items such as tea and tobacco from a diachronical perspective, supporting historical investigation with corpus-based evidence.
Exploring Social Sciences Archives with Explainable Document Linkage through Question Generation (2023)

This paper proposes a new approach for exploring digitized humanities and social sciences collections based on explainable links built from questions. Our experiments show the quality of our automatically generated questions and their relevance in a local context as well as the originality of the links produced by embeddings based on these questions. Analyses have also been performed to understand the types of questions generated on our corpus, and the related uses that can enrich the exploration. The relationships between the co-references and the questions generated, and the answers extracted from the text were also discussed and open a path for future improvements for our system in their resolution.
Wartime Media Monitor (WarMM-2022): A Study of Information Manipulation on Russian Social Media during the Russia-Ukraine War (2023)

This study relies on natural language processing to explore the nature of online communication in Russia during the war on Ukraine in 2022. The analysis of a large corpus of publications in traditional media and on social media identifies massive state interventions aimed at manipulating public opinion. The study relies on expertise in media studies and political science to trace the major themes and strategies of the propagandist narratives on three major Russian social media platforms over several months as well as their perception by the users. Distributions of several keyworded pro-war and anti-war topics are examined to reveal the cross-platform specificity of social media audiences. We release WarMM-2022, a 1.7M posts corpus. This corpus includes publications related to the Russia-Ukraine war, which appeared in Russian mass media and on social networks between February and September 2022. The corpus can be useful for the development of NLP approaches to propaganda detection and subsequent studies of propaganda campaigns in social sciences in addition to traditional methods, such as content analysis, focus groups, surveys, and experiments.
Towards a More In-Depth Detection of Political Framing (2023)

In social sciences, recent years have witnessed a growing interest in applying NLP approaches to automatically detect framing in political discourse. However, most NLP studies by now focus heavily on framing effect arising from topic coverage, whereas framing effect arising from subtle usage of linguistic devices remains understudied. In a collaboration with political science researchers, we intend to investigate framing strategies in German newspaper articles on the {\textquotedblleft}European Refugee Crisis{\textquotedblright}. With the goal of a more in-depth framing analysis, we not only incorporate lexical cues for shallow topic-related framing, but also propose and operationalize a variety of framing-relevant semantic and pragmatic devices, which are theoretically derived from linguistics and political science research. We demonstrate the influential role of these linguistic devices with a large-scale quantitative analysis, bringing novel insights into the linguistic properties of framing.
Named Entity Annotation Projection Applied to Classical Languages (2023)

In this study, we demonstrate how to apply cross-lingual annotation projection to transfer named-entity annotations to classical languages for which limited or no resources and annotated texts are available, aiming to enrich their NER training datasets and train a model to perform NER tagging. Our method uses sentence-level aligned parallel corpora ancient texts and the translation in a modern language, for which high-quality off-the-shelf NER systems are available. We automatically annotate the text of the modern language and employ a state-of-the-art neural word alignment system to find translation equivalents. Finally, we transfer the annotations to the corresponding tokens in the ancient texts using a direct projection heuristic. We applied our method to ancient Greek, Latin, and Arabic using the Bible with the English translation as a parallel corpus. We used the resulting annotations to enhance the performance of an existing NER model for ancient Greek
English and German pop song lyrics: Towards a contrastive textology (2023)

The present contribution offers a contrastive corpus-based analysis of English and German pop lyrics. It conceptualizes lyrics as a specific text type/register and tries to identify cross-linguistic commonalities and differences. As empirical base, it uses corpora that represent the lyrics of commercially highly successful pop songs in Anglophone and German contexts. Given the similar sociocultural functions and production circumstances of English and German lyrics, the study starts from the assumption that a large-scale linguistic overlap can be traced. While indeed cross-linguistic convergence is found especially for lexical patterns in terms of topic choice, the analysis also reveals a common property of conveying a conversational feel through lexicogrammatical means. However, given the differing typological make-up of the languages contrasted, fine-grained differences emerge as regards the ways conversationality/informality is established in pop lyrics as a performed text type.
Keyness in song lyrics: Challenges of highly clumpy data (2023)

Computer-assisted stylistic analyses regularly employ the calculation of keywords. We show that the inclusion of a separate dispersion measure in addition to a frequency measure into keyword analysis (or more generally: keyness analysis), as proposed by Gries (2021), is a necessary extension of said analyses. Using texts from the German Songkorpus, we demonstrate that traditional keyword calculations using only frequency measures lead to spurious results. Determining keywords by both measuring a word`s frequency and its dispersion in comparison to a reference corpus gives a more realistic view. This is especially relevant for our corpus, since song lyrics turn out to be extraordinarily clumpy data: Words that are very frequent in one artist`s subcorpus typically only occur in a few or even just a single one of their songs due to widespread word repetition within songs, e.g., in choruses. Song lyrics in our dataset are shown to not feature words that can be considered key at all. Our contribution is twofold: (1) We demonstrate the utility of Gries' (2021) approach and (2) interpret the (lack of) results in terms of a genre-specific property which is that song lyrics are lexically autonomous works of art.
Ist alte Schule oldschool? Zum \textquotedblleftNutzen\textquotedblright von Anglizismen in Deutschraptexten (2023)

Der vorliegende Beitrag vergleicht die Verwendung der anglizistischen Nomination old school und der nativen Entsprechung Alte Schule im Hip-Hop-Subkorpus des Songkorpus (Schneider 2020). Dieser Vergleich erfolgt auf zwei Ebenen: Zum einen wird die diskurs-spezifische Verwendung anhand eines adaptierten Analyse-Frameworks f{\"u}r Hip-Hop-Texte von Androutsopoulos und Scholz (2002) untersucht, zum anderen wird der syntaktische und morphologische Gebrauch in den Deutschraptexten analysiert. Dabei zeigt sich, dass es jeweils spezifische Verwendungstendenzen auf diskursiver Ebene gibt, die wesentlichsten Unterschiede aber in der syntaktischen und morphologischen Verwendung auftreten, allen voran in der h{\"o}heren Produktivit{\"a}t der anglizistischen Nomination. Es wird daf{\"u}r argumentiert, dass sich dies unter anderem auf sprachstrukturelle bzw. wortformale Spezifika des Englischen zur{\"u}ckf{\"u}hren l{\"a}sst, wie den nicht vorhandenen Flexionssuffixen der Adjektive. Damit werden die in der Anglizismenforschung etablierten {\"U}berlegungen zu Verwendungsgr{\"u}nden um eine simple, aber gegebenenfalls folgenreiche Beobachtung erg{\"a}nzt, die sich vor allem bei den sprach{\"o}konomischen Ans{\"a}tzen einordnen l{\"a}sst. Schlie{\ss}lich wird dar{\"u}ber auf diskursiver Ebene wiederum auch ein Bezug zu terminologischen Vorteilen hergeleitet: Trotz flexibler Verwendung wird das schriftliche Abbild bei Wortbildungen geschont (Oldschoolstyle, Oldschool-Aufnahmen, Oldschooler), was f{\"u}r die Wiedererkennbarkeit des Diskurselements {--} neben der zus{\"a}tzlichen Auszeichnung durch die Eigenschaft {\quotesinglbase}fremdsprachig{\textquoteleft} {--} zutr{\"a}glich sein k{\"o}nnte.
\textquotedblleftBeinahe-ums-Leben-kommen-in-Regenpf\"utzen\textquotedblright und \textquotedblleftChauvi-Macho-Macker-Stuss\textquotedblright -- kreative Wortbildungen in Songtexten (2023)

Im Zentrum dieses Beitrags steht die Analyse kreativer Wortbildungsprodukte in Songtexten. Der Fokus liegt somit bewusst auf solchen Wortbildungen, die nicht den Weg ins Lexikon finden, sondern gerade aufgrund ihres okkasionellen Charakters einen erh{\"o}hten Grad an Expressivit{\"a}t aufweisen, der dann gezielt f{\"u}r die spezifische kreative Qualit{\"a}t von Songtexten genutzt wird. Solche okkasionellen komplexen W{\"o}rter, die sich in theoretischer Hinsicht innerhalb der Dom{\"a}ne der {\quotesinglbase}Extravagant Morphology{\textquoteleft} verorten lassen, werden {\"u}ber das Kriterium der Wortl{\"a}nge aus dem Songkorpus herausgefiltert und im Anschluss hinsichtlich ihrer formalen sowie semantisch-pragmatischen Besonderheiten analysiert. Im Vordergrund steht dabei die Frage, wodurch die Kreativit{\"a}t der insgesamt 183 Bildungen des Untersuchungskorpus getriggert wird. Die Analyse zeigt, dass expressive Effekte in Songtexten offenbar sowohl durch die Verwendung markierter Wortbildungsmuster als auch durch den R{\"u}ckgriff auf {\quotesinglbase}auff{\"a}llige{\textquoteleft} Lexik erzeugt werden. Zum einen ist der Anteil markierter Wortbildungsmuster wie der Phrasenkomposition und anderer phrasaler Wortbildungen gegen{\"u}ber klassischen Textsorten wie Zeitungstexten deutlich erh{\"o}ht. Zum anderen wird durch die Verwendung einer umgangssprachlichen, vulg{\"a}ren, brutalen oder poetischen Lexik, aber auch mit unmarkierten Wortbildungsmustern wie der prototypischen Determinativkomposition, Aufmerksamkeit erregt. Insgesamt erweist sich das Songkorpus dabei als wahre Fundgrube f{\"u}r kreative Wortbildungsprodukte.
Phraseme im Songkorpus: Etabliertes in Anti-Establishment-Texten (2023)

Das Songkorpus erlaubt Einblicke in bestimmte gesellschaftliche Diskurse, die in anderen Sprachkorpora weniger zur Geltung kommen. Das zeigt sich auch bei der Analyse von Phrasemen im Songkorpus. Phraseme sind etablierte Wortkombinationen; sie konservieren kollektives Wissen, kollektive Kultur. Element of Crime, Fettes Brot, Udo Lindenberg, Stefan Stoppok, Konstantin Wecker, Marius M{\"u}ller-Westernhagen, die Autoren meines kleinen Teilkorpus, sind Anti-Establishment und alles andere als konservativ. Zwar verwenden sie h{\"a}ufig Phraseme verschiedenster Struktur und Art, karikieren sie aber auch h{\"a}ufig, spielen l{\"a}ssig mit ihnen, hinterfragen ihre Bedeutung, ver{\"a}ndern ihre Bedeutung. Ihre spezielle Haltung bedingt spezielle Phraseme und spezielle Phrasemvarianten.