Clickbait Classification and Spoiling Using Natural Language Processing

Clickbait is the practice of engineering titles to incentivize readers to click through to articles. Such titles with sensationalized language reveal as little information as possible. Occasionally, clickbait will be intentionally misleading, so natural language processing (NLP) can scan the article and answer the question posed by the clickbait title, or spoil it. We tackle two tasks: classifying the clickbait into one of 3 types (Task 1), and spoiling the clickbait (Task 2). For Task 1, we propose two binary classifiers to determine the final spoiler type. For Task 2, we experiment with two approaches: using a question-answering model to identify the span of text of the spoiler, and using a large language model (LLM) to generate the spoiler. Because the spoiler is contained in the article, we frame the second task as a question-answering approach for identifying the starting and ending positions of the spoiler. We created models for Task 1 that were better than the baselines proposed by the dataset authors and engineered prompts for Task 2 that did not perform as well as the baselines proposed by the dataset authors due to the evaluation metric performing worse when the output text is from a generative model as opposed to an extractive model.Comment: 7 pages, 2 figures, 3 tables, 1 Appendix (3 Sections

Effects of Different Types of Noise on Foreign Accent Adaptation

Understanding foreign-accented speech can be difficult. Comprehension can be further compromised by environmental noise. Previous research has shown that listeners are able to adapt rapidly to a foreign accent. The present study examines how foreign accent (FA) adaptation is affected by two kinds of noise: speech-shaped white noise and competing speech. Native English listeners heard blocks of sentences produced by native-accented or foreign-accented talkers (Korean, Spanish) mixed with either type of noise, and indicated if the word written on the screen and the last word they heard were the same by pressing a button. Results show that listener responses were more accurate (though slower) when sentences were mixed with competing speech than with speech-shaped white noise. These findings suggest that while competing speech made word recognition more effortful, ultimately it was less disruptive than white noise for FA adaptation.Linguistic

Speaking style adaptations across the lifespan

In everyday life, speech communication occurs in suboptimal or adverse conditions (e.g., talking to a listener who is hard of hearing; presence of noise in the environment). This study examines how talkers change the way they speak in response to challenging communicative situations. We investigate what consequences such changes have on intelligibility and whether speaking style adaptations vary with the talker's age.Linguistic

TRATTAMENTI DI PRIMA LINEA PER LA LEUCEMIA LINFATICA CRONICA: ANALISI DI 7 STUDI BASATI SUL RESTRICTED MEAN SURVIVAL TIME E VALUTAZIONE DELLA SPESA PER SINGOLO PAZIENTE SU BASE REGIONALE

Negli ultimi anni l'approccio terapeutico nel trattamento di prima linea dei pazienti con leucemia linfatica cronica (LLC) ha subito un significativo cambiamento grazie allo sviluppo di nuove molecole in grado di interferire con i processi essenziali per la sopravvivenza e la crescita del linfocita neoplastico, ovvero: gli inibitori della tirosina chinasi di Bruton (BTK) (ibrutinib, acalabrutinib), della fosfatidilinositolo-3-chinasi δ (PI-3kδ) (idelalisib) e l'inibitore della BCL-2 (venetoclax). Questi nuovi agenti hanno subito un upgrade passando da "grande opzione terapeutica" a "scelta preferenziale" per tutte le linee di trattamento, dopo che numerosi studi clinici randomizzati hanno dimostrato la loro superiorità rispetto ai regimi di chemioimmunoterapia convenzionale. Il principale obiettivo di questo lavoro è quello di confrontare il profilo di efficacia delle terapie di prima linea per la LLC attualmente disponibili, utilizzando un parametro relativamente nuovo, il restricted mean survival time (RMST), proposto per migliorare l’analisi delle curve di sopravvivenza. Inoltre, allo scopo di confrontare i risultati clinici con i rispettivi dati di costo, è stata condotta un’analisi farmacoeconomica

Subjective discourse analysis

Understanding the discourse of a text necessitates an understanding of how propositions are structured and organized, a challenging task for computational models. But discourse also involves ambiguity, which sometimes opens the door for subjective interpretations. In this thesis, I first examine how discourse information can be abstracted for effective use by computational models on downstream NLP tasks. Because these methods suffer from data scarcity, I explore other ways to learn discourse structure. An unsupervised approach is not able to learn the rich information encoded in discourse, while a manual annotation of discourse rediscovers the possibility of multiple interpretations. I seize on this result to focus on eliciting and analyzing subjective interpretations of discourse.Linguistic

Testing the usefulness of RST and more general representations for discourse analysis across domains and applications

Discourse analysis is a task with enormous potential but is often met with lukewarm results. This report explores how well Rhetorical Structure Theory (RST) and more general representations of discourse can generalize across domains and tasks, and the validity of their underlying assumptions. Our first study attempts to uncover issues in Rhetorical Structure Theory (RST) discourse parsing by starting at the first step of discourse segmentation, and evaluate in the medical domain. Errors on our novel, small-scale medical corpus reveal differences at lower linguistic levels that affect the discourse segmenter, and point to problem areas in the way RST was operationalized. Our second study focuses on more general representations of discourse that are learned by the model, and that have only a simple constraint of forming a dependency tree. We find these latent trees in fact do not represent discourse and focus instead on lexical cues. We propose a variant of this model that is able to learn deeper structures, but conclude that a different task which makes more use of discourse may be needed in order to produce more discourse-like structuresLinguistic