Yuri Bizzoni


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Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features
Yuri Bizzoni | Pascale Moreira | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Over the years, the task of predicting reader appreciation or literary quality has been the object of several studies, but it remains a challenging problem in quantitative literary studies and computational linguistics alike, as its definition can vary a lot depending on the genre, the adopted features and the annotation system. This paper attempts to evaluate the impact of sentiment arc modelling versus more classical stylometric features for user-ratings of novels. We run our experiments on a corpus of English language narrative literary fiction from the 19th and 20th century, showing that syntactic and surface-level features can be powerful for the study of literary quality, but can be outperformed by sentiment-characteristics of a text.

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Good Reads and Easy Novels: Readability and Literary Quality in a Corpus of US-published Fiction
Yuri Bizzoni | Pascale Moreira | Nicole Dwenger | Ida Lassen | Mads Thomsen | Kristoffer Nielbo
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

In this paper, we explore the extent to which readability contributes to the perception of literary quality as defined by two categories of variables: expert-based (e.g., Pulitzer Prize, National Book Award) and crowd-based (e.g., GoodReads, WorldCat). Based on a large corpus of modern and contemporary fiction in English, we examine the correlation of a text’s readability with its perceived literary quality, also assessing readability measures against simpler stylometric features. Our results show that readability generally correlates with popularity as measured through open platforms such as GoodReads and WorldCat but has an inverse relation with three prestigious literary awards. This points to a distinction between crowd- and expert-based judgments of literary style, as well as to a discrimination between fame and appreciation in the reception of a book.

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Fractality of informativity in 300 years of English scientific writing
Yuri Bizzoni | Stefania Degaetano-ortlieb
Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

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.

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Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles
Pascale Moreira | Yuri Bizzoni | Kristoffer Nielbo | Ida Marie Lassen | Mads Thomsen
Proceedings of the The 5th Workshop on Narrative Understanding

Predicting literary quality and reader appreciation of narrative texts are highly complex challenges in quantitative and computational literary studies due to the fluid definitions of quality and the vast feature space that can be considered when modeling a literary work. This paper investigates the potential of sentiment arcs combined with topical-semantic profiling of literary narratives as indicators for their literary quality. Our experiments focus on a large corpus of 19th and 20the century English language literary fiction, using GoodReads’ ratings as an imperfect approximation of the diverse range of reader evaluations and preferences. By leveraging a stacked ensemble of regression models, we achieve a promising performance in predicting average readers’ scores, indicating the potential of our approach in modeling literary quality.


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Fractality of sentiment arcs for literary quality assessment: The case of Nobel laureates
Yuri Bizzoni | Kristoffer Laigaard Nielbo | Mads Rosendahl Thomsen
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities

In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the long-term memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier.

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Predicting Literary Quality How Perspectivist Should We Be?
Yuri Bizzoni | Ida Marie Lassen | Telma Peura | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

Approaches in literary quality tend to belong to two main grounds: one sees quality as completely subjective, relying on the idiosyncratic nature of individual perspectives on the apperception of beauty; the other is ground-truth inspired, and attempts to find one or two values that predict something like an objective quality: the number of copies sold, for example, or the winning of a prestigious prize. While the first school usually does not try to predict quality at all, the second relies on a single majority vote in one form or another. In this article we discuss the advantages and limitations of these schools of thought and describe a different approach to reader’s quality judgments, which moves away from raw majority vote, but does try to create intermediate classes or groups of annotators. Drawing on previous works we describe the benefits and drawbacks of building similar annotation classes. Finally we share early results from a large corpus of literary reviews for an insight into which classes of readers might make most sense when dealing with the appreciation of literary quality.


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Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?
Yuri Bizzoni | Ekaterina Lapshinova-Koltunski
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

The present paper deals with a computational analysis of translationese in professional and student English-to-German translations belonging to different registers. Building upon an information-theoretical approach, we test translation conformity to source and target language in terms of a neural language model’s perplexity over Part of Speech (PoS) sequences. Our primary focus is on register diversification vs. convergence, reflected in the use of constructions eliciting a higher vs. lower perplexity score. Our results show that, against our expectations, professional translations elicit higher perplexity scores from a target language model than students’ translations. An analysis of the distribution of PoS patterns across registers shows that this apparent paradox is the effect of higher stylistic diversification and register sensitivity in professional translations. Our results contribute to the understanding of human translationese and shed light on the variation in texts generated by different translators, which is valuable for translation studies, multilingual language processing, and machine translation.

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Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences
Yuri Bizzoni | Telma Peura | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

e explore the correlation between the sentiment arcs of H. C. Andersen’s fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story’s overall sentimental trend as predictive per se, but we focus on its coherence and predictability over time as represented by the arc’s Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a “sweet spot” for literary appreciation.

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The diffusion of scientific terms – tracing individuals’ influence in the history of science for English
Yuri Bizzoni | Stefania Degaetano-Ortlieb | Katrin Menzel | Elke Teich
Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Tracing the influence of individuals or groups in social networks is an increasingly popular task in sociolinguistic studies. While methods to determine someone’s influence in shortterm contexts (e.g., social media, on-line political debates) are widespread, influence in longterm contexts is less investigated and may be harder to capture. We study the diffusion of scientific terms in an English diachronic scientific corpus, applying Hawkes Processes to capture the role of individual scientists as “influencers” or “influencees” in the diffusion of new concepts. Our findings on two major scientific discoveries in chemistry and astronomy of the 18th century reveal that modelling both the introduction and diffusion of scientific terms in a historical corpus as Hawkes Processes allows detecting patterns of influence between authors on a long-term scale.

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Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces
Ekaterina Lapshinova-Koltunski | Heike Przybyl | Yuri Bizzoni
Proceedings of the 2nd Workshop on Computational Approaches to Discourse

In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings. Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting. To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-to-interpreting aligned corpora. Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting. We also observe different trends with regard to the discourse relation types.

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Proceedings for the First Workshop on Modelling Translation: Translatology in the Digital Age
Yuri Bizzoni | Elke Teich | Cristina España-Bonet | Josef van Genabith
Proceedings for the First Workshop on Modelling Translation: Translatology in the Digital Age

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Found in translation/interpreting: combining data-driven and supervised methods to analyse cross-linguistically mediated communication
Ekaterina Lapshinova-Koltunski | Yuri Bizzoni | Heike Przybyl | Elke Teich
Proceedings for the First Workshop on Modelling Translation: Translatology in the Digital Age


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How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech
Yuri Bizzoni | Tom S Juzek | Cristina España-Bonet | Koel Dutta Chowdhury | Josef van Genabith | Elke Teich
Proceedings of the 17th International Conference on Spoken Language Translation

Translationese is a phenomenon present in human translations, simultaneous interpreting, and even machine translations. Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear. This study analyzes translationese patterns in translation, interpreting, and machine translation outputs in order to explore possible reasons. In our analysis we – (i) detail two non-invasive ways of detecting translationese and (ii) compare translationese across human and machine translations from text and speech. We find that machine translation shows traces of translationese, but does not reproduce the patterns found in human translation, offering support to the hypothesis that such patterns are due to the model (human vs machine) rather than to the data (written vs spoken).

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Sky + Fire = Sunset. Exploring Parallels between Visually Grounded Metaphors and Image Classifiers
Yuri Bizzoni | Simon Dobnik
Proceedings of the Second Workshop on Figurative Language Processing

This work explores the differences and similarities between neural image classifiers’ mis-categorisations and visually grounded metaphors - that we could conceive as intentional mis-categorisations. We discuss the possibility of using automatic image classifiers to approximate human metaphoric behaviours, and the limitations of such frame. We report two pilot experiments to study grounded metaphoricity. In the first we represent metaphors as a form of visual mis-categorisation. In the second we model metaphors as a more flexible, compositional operation in a continuous visual space generated from automatic classification systems.


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The Effect of Context on Metaphor Paraphrase Aptness Judgments
Yuri Bizzoni | Shalom Lappin
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN’s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.

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Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings
Yuri Bizzoni | Stefania Degaetano-Ortlieb | Katrin Menzel | Pauline Krielke | Elke Teich
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

The paper showcases the application of word embeddings to change in language use in the domain of science, focusing on the Late Modern English period (17-19th century). Historically, this is the period in which many registers of English developed, including the language of science. Our overarching interest is the linguistic development of scientific writing to a distinctive (group of) register(s). A register is marked not only by the choice of lexical words (discourse domain) but crucially by grammatical choices which indicate style. The focus of the paper is on the latter, tracing words with primarily grammatical functions (function words and some selected, poly-functional word forms) diachronically. To this end, we combine diachronic word embeddings with appropriate visualization and exploratory techniques such as clustering and relative entropy for meaningful aggregation of data and diachronic comparison.

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Some steps towards the generation of diachronic WordNets
Yuri Bizzoni | Marius Mosbach | Dietrich Klakow | Stefania Degaetano-Ortlieb
Proceedings of the 22nd Nordic Conference on Computational Linguistics

We apply hyperbolic embeddings to trace the dynamics of change of conceptual-semantic relationships in a large diachronic scientific corpus (200 years). Our focus is on emerging scientific fields and the increasingly specialized terminology establishing around them. Reproducing high-quality hierarchical structures such as WordNet on a diachronic scale is a very difficult task. Hyperbolic embeddings can map partial graphs into low dimensional, continuous hierarchical spaces, making more explicit the latent structure of the input. We show that starting from simple lists of word pairs (rather than a list of entities with directional links) it is possible to build diachronic hierarchical semantic spaces which allow us to model a process towards specialization for selected scientific fields.


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Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks
Yuri Bizzoni | Shalom Lappin
Proceedings of the Workshop on Figurative Language Processing

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75% accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.

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Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
Yuri Bizzoni | Mehdi Ghanimifard
Proceedings of the Workshop on Figurative Language Processing

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.


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“Deep” Learning : Detecting Metaphoricity in Adjective-Noun Pairs
Yuri Bizzoni | Stergios Chatzikyriakidis | Mehdi Ghanimifard
Proceedings of the Workshop on Stylistic Variation

Metaphor is one of the most studied and widespread figures of speech and an essential element of individual style. In this paper we look at metaphor identification in Adjective-Noun pairs. We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy. In specific, the approach presented in this paper is based on two ideas: a) transfer learning via using pre-trained vectors representing adjective noun pairs, and b) a neural network as a model of composition that predicts a metaphoricity score as output. We present several different architectures for our system and evaluate their performances. Variations on dataset size and on the kinds of embeddings are also investigated. We show considerable improvement over the previous approaches both in terms of accuracy and w.r.t the size of annotated training data.

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Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase
Yuri Bizzoni | Shalom Lappin
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers


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Ancient Greek WordNet Meets the Dynamic Lexicon: the Example of the Fragments of the Greek Historians
Monica Berti | Yuri Bizzoni | Federico Boschetti | Gregory R. Crane | Riccardo Del Gratta | Tariq Yousef
Proceedings of the 8th Global WordNet Conference (GWC)

The Ancient Greek WordNet (AGWN) and the Dynamic Lexicon (DL) are multilingual resources to study the lexicon of Ancient Greek texts and their translations. Both AGWN and DL are works in progress that need accuracy improvement and manual validation. After a detailed description of the current state of each work, this paper illustrates a methodology to cross AGWN and DL data, in order to mutually score the items of each resource according to the evidence provided by the other resource. The training data is based on the corpus of the Digital Fragmenta Historicorum Graecorum (DFHG), which includes ancient Greek texts with Latin translations.

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Non-Literal Text Reuse in Historical Texts: An Approach to Identify Reuse Transformations and its Application to Bible Reuse
Maria Moritz | Andreas Wiederhold | Barbara Pavlek | Yuri Bizzoni | Marco Büchler
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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From distributions to labels: A lexical proficiency analysis using learner corpora
David Alfter | Yuri Bizzoni | Anders Agebjörn | Elena Volodina | Ildikó Pilán
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition


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The Making of Ancient Greek WordNet
Yuri Bizzoni | Federico Boschetti | Harry Diakoff | Riccardo Del Gratta | Monica Monachini | Gregory Crane
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes the process of creation and review of a new lexico-semantic resource for the classical studies: AncientGreekWordNet. The candidate sets of synonyms (synsets) are extracted from Greek-English dictionaries, on the assumption that Greek words translated by the same English word or phrase have a high probability of being synonyms or at least semantically closely related. The process of validation and the web interface developed to edit and query the resource are described in detail. The lexical coverage of Ancient Greek WordNet is illustrated and the accuracy is evaluated. Finally, scenarios for exploiting the resource are discussed.