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Articles 1 - 6 of 6
Full-Text Articles in Computational Linguistics
Gpt Assisted Annotation Of Rhetorical And Linguistic Features For Interpretable Propaganda Technique Detection In News Text., Kyle Hamilton, Bojan Bozic, Luca Longo
Gpt Assisted Annotation Of Rhetorical And Linguistic Features For Interpretable Propaganda Technique Detection In News Text., Kyle Hamilton, Bojan Bozic, Luca Longo
Articles
While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box'' solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set …
Is Neuro-Symbolic Ai Meeting Its Promises In Natural Language Processing? A Structured Review, Kyle Hamilton, Kyle Hamilton, Aparna Nayak, Bojan Bozic, Luca Longo
Is Neuro-Symbolic Ai Meeting Its Promises In Natural Language Processing? A Structured Review, Kyle Hamilton, Kyle Hamilton, Aparna Nayak, Bojan Bozic, Luca Longo
Articles
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its …
Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher
Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher
Articles
Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with …
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Articles
Errors in visual perception may cause problems in situated dialogues. We investigated this problem through an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot.We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions.We then introduced different methods for the user to request information about the robot’s understanding of the environment. We quantify the impact of perception errors on the dialogues, and investigate resolution attempts by users at a structural level and at the level of referring expressions.
Perception Based Misunderstandings In Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Perception Based Misunderstandings In Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Articles
In a situated dialogue, misunderstandings may arise if the participants perceive or interpret the environment in different ways. In human-computer dialogue this may be due the sensor errors. We present an experiment system and a series of experiments in which we investigate this problem.
Applying Computational Models Of Spatial Prepositions To Visually Situated Dialog, John D. Kelleher, Fintan Costello
Applying Computational Models Of Spatial Prepositions To Visually Situated Dialog, John D. Kelleher, Fintan Costello
Articles
This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact …