Когнітивне дослідження концепту «Подорож» у поезії Відад Бенмуса

The concept of motion is central to the human cognition and it is universally studied in cognitive linguistics. This research paper investigates concept of motion, with special reference to traveling, in the poetry of Widad Benmoussa. It mainly focuses on the cognitive dimensions underlying the metaphorical representation of traveling. To this end, the research conducts a semi-automated analysis of a corpus representing Widad’s poetic collections. MetaNet’s physical path is mainly used to reveal the cognitive respects of traveling. The personae the poetess assigns are found to pursue a dynamic goal through activation of several physical paths. During the unstable romantic relations, several travel impediments are met. Travel stops and detours, travel companions, paths in journey as well as changing travel destinations are the most stressed elements of ‘Traveling’ respects. With such a described high frequency of sudden departures and hopping, the male persona the poetess assigns evinces typical features of 'wanderlust' or dromomania. Концепція руху є центральною для людського пізнання й сьогодні вона є поширеним предметом вивчення в когнітивній лінгвістиці. У цій статті представлене дослідження концепту руху, а особливу увагу приділено темі подорожі у віршах Відад Бенмуса, а саме: когнітивним вимірам, що лежать в основі метафоричного представлення мандрівки. Для досягнення цієї мети було проведено напівавтоматичний аналіз корпусу поетичних збірок Відад Бенмуса. Фізичний шлях MetaNet головно застосовано для виявлення когнітивних аспектів подорожі. Установлено, що персонажі, про яких пише поетеса, переслідують динамічну мету через активацію декількох фізичних шляхів. Під час їхніх нестабільних романтичних стосунків, трапляються перешкоди на шляху до подорожей. З’ясовано, що найбільш виділеними елементами аспектів «подорожі» є: зупинки й об’їзди під час подорожей, супутники, шлях в дорозі, а також зміна туристичних напрямків. Ураховуючи описану високу частоту раптових від’їздів і поворотів, можна стверджувати, що особистість чоловіка, про якого пише поетеса, характеризують такі основні риси, як «нестримний потяг до мандрівки» або дромоманія

Combining quantitative narrative analysis and predictive modeling - an eye tracking study

As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)

Ring That Bell : A Corpus and Method for Multimodal Metaphor Detection in Videos

Peer reviewe

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Exploring figurative language recognition: a comprehensive study of human and machine approaches

Treballs Finals de Grau de Llengües i Literatures Modernes. Facultat de Filologia. Universitat de Barcelona. Curs: 2022-2023. Tutora: Elisabet Comelles Pujadas[eng] Figurative language (FL) plays a significant role in human communication. Understanding and interpreting FL is essential for humans to fully grasp the intended message, appreciate cultural nuances, and engage in effective interaction. For machines, comprehending FL presents a challenge due to its complexity and ambiguity. Enabling machines to understand FL has become increasingly important in sentiment analysis, text classification, and social media monitoring, for instance, benefits from accurately recognizing figurative expressions to capture subtle emotions and extract meaningful insights. Machine translation also requires the ability to accurately convey FL to ensure translations reflect the intended meaning and cultural nuances. Therefore, developing computational methods to enable machines to understand and interpret FL is crucial. By bridging the gap between human and machine understanding of FL, we can enhance communication, improve language-based applications, and unlock new possibilities in human-machine interactions. Keywords: figurative language, NLP, human-machine communication.[cat] El Llenguatge Figuratiu (LF) té un paper important en la comunicació humana. Per entendre completament els missatges, apreciar els matisos culturals i la interacció efectiva, és necessària la capacitat d'interpretar el LF. No obstant això, els ordinadors tenen dificultats per entendre la LF a causa de la seva complexitat i ambigüitat. És crític que els ordinadors siguin capaços de reconèixer el LF, especialment en àrees com l'anàlisi de sentiments, la classificació de textos i la supervisió de les xarxes socials. El reconeixement precís del LF permet capturar emocions i extreure idees semàntiques. La traducció automàtica també requereix una representació precisa del LF per reflectir el significat previst i els matisos culturals. Per tant, és rellevant desenvolupar mètodes computacionals que ajudin els ordinadors a comprendre i interpretar el LF. Fer un pont entre la comprensió humana i màquina del LF pot millorar la comunicació, desenvolupar aplicacions de llenguatge i obrir noves possibilitats per a la interacció home-màquina. Paraules clau: llenguatge figuratiu, processament del llenguatge natural, interacció home-màquina

Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art