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We perform a mixed-method frame semantics-based analysis on a dataset of more than 49,000 sentences collected from 5846 news articles that mention AI. The dataset covers the twelve-month period centred around the launch of OpenAI’s chatbot ChatGPT and is collected from the most visited open-access English-language news publishers. Our findings indicate that during the six months succeeding the launch, media attention rose tenfold—from already historically high levels. During this period, discourse has become increasingly centred around experts and political leaders, and AI has become more closely associated with dangers and risks. A deeper review of the data also suggests a qualitative shift in the types of threat AI is thought to represent, as well as the anthropomorphic qualities ascribed to it.

We introduce opinion units, a contribution to the field Aspect-Based Sentiment Analysis (ABSA) that extends aspect- sentiment pairs by including substantiating excerpts, derived through hybrid abstractive-extractive summarisation. The goal is to provide fine-grained information without sacrificing succinctness and abstraction. Evaluations on review datasets demonstrate that large language models (LLMs) can accurately extract opinion units through few-shot learning. The main types of errors are providing incomplete contexts for opinions and and mischaracterising objective statements as opinions. The method reduces the need for labelled data and allows the LLM to dynamically define aspect types. As a practical evaluation, we present a case study on similarity search across academic datasets and public review data. The results indicate that searches leveraging opinion units are more successful than those relying on traditional data-segmentation strategies, showing robustness across datasets and embeddings.

The Visual Object Tracking Segmentation VOTS2024 challenge is the twelfth annual tracker benchmarking activity of the VOT initiative. This challenge consolidates the new tracking setup proposed in VOTS2023, which merges short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. Two sub-challenges are considered. The VOTS2024 standard challenge, focusing on classical objects and the VOTSt2024, which considers objects undergoing a topological transformation. Both challenges use the same performance evaluation methodology. Results of 28 submissions are presented and analyzed. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available on the website (https://www.votchallenge.net/vots2024/).

In programmatic advertising, bids are increasingly based on knowledge of the surrounding media context. This shift toward contextual advertising is in part a counter-reaction to the current dependency on personal data, which is problematic from legal and ethical standpoints. The transition is accelerated by developments in artificial intelligence (AI), which allow for a deeper semantic analysis of the context and, by extension, more effective ad placement. We survey existing literature on the influence of context on the reception of an advertisement, focusing on three context factors: the applicability of the content and the ad, the affective tone of the content, and the involvement of the consumer. We then discuss how AI can leverage these priming effects to optimize ad placement through techniques such as reinforcement learning, data clustering, and sentiment analysis. This helps close the gap between the state of the art in advertising technology and the AI-driven targeting methodologies described in prior academic research.

Online advertising opportunities are bought and sold in automated auctions driven by real-time bidding. In the case of contextual advertising, the size of a bid is informed by the media context in which the ad will be displayed. In contrast to personalised advertising, contextual advertising is better aligned with privacy acts such as GDPR and CCPA. We investigate how reinforcement learning with human feedback can help optimise contextual advertising under budget constraints. We propose a dynamic epsilon-greedy algorithm that considers the rate of budget consumption during a finite transaction time. The goal is to maximise long-term rewards in a sustainable manner. Our comparative evaluation of fundamental reinforcement learning algorithms on real data suggests that the approach is feasible and effective.

A regular unranked tree folding consists of a regular unranked tree language and a folding operation that merges (i.e., folds) selected nodes of a tree to form a graph; the combination is a formal device for representing graph languages. If, in the process of folding, the order among edges is discarded so that the result is an unordered graph, then two applications of a fold operation are enough to make the associated parsing problem NP-complete. However, if the order is kept, then the problem is solvable in non-uniform polynomial time. In this paper, we address the remaining case, where only one fold operation is applied, but the order among the edges is discarded. We show that, under these conditions, the problem is solvable in non-uniform polynomial time.

This article contributes to research on media-context effects by studying how ads are assessed when positioned alongside news articles that evoke negative emotions in readers. We find that in general, negative emotion does not influence advertising evaluation. Contrary to industry claims, the perceived source credibility of the news site is not found to moderate the effects of negative content. However, on its own, the credibility of the news site improves ad evaluations. Furthermore, high applicability between article and ad can enhance ad recognition and produce a weak negative effect on attitudes towards ads and brands. Our results provide evidence against the industry practice of avoiding negative news due to concerns over spill-over effects. Marketers should focus advertising to credible news sites and, when appropriate, avoid negative articles with high applicability to the advertised product and brand.

Dietze's conjecture concerns the problem of equipping a tree automaton M with weights to make it probabilistic, in such a way that the resulting automaton N predicts a given corpus C as accurately as possible. The conjecture states that the accuracy cannot increase if the states in M are merged with respect to an equivalence relation ∼ so that the result is a smaller automaton M^∼. Put differently, merging states can never improve predictions. This is under the assumption that both M and M^∼ are bottom-up deterministic and accept every tree in C. We prove that the conjecture holds, using a construction that turns any probabilistic version N^∼ of M^∼ into a probabilistic version N of M, such that N assigns at least as great a weight to each tree in C as N^∼ does.

We explore computational methods for perceived agency attribution in natural language data. We consider ‘agency’ as the freedom and capacity to act, and the corresponding Natural Language Processing (NLP) task involves automatically detecting attributions of agency to entities in text. Our theoretical framework draws on semantic frame analysis, role labelling and related techniques. In initial experiments, we focus on the perceived agency of AI systems. To achieve this, we analyse a dataset of English-language news coverage of AI-related topics, published within one year surrounding the release of the Large Language Model-based service ChatGPT, a milestone in the general public’s awareness of AI. Building on this, we propose a schema to annotate a dataset for agency attribution and formulate additional research questions to answer by applying NLP models.

Order-preserving DAG grammars (OPDGs) is a formalism for representing languages of structurally restricted graphs. As demonstrated in [17], they are sufficiently expressive to model abstract meaning representations in natural language processing, a graph-based form of semantic representation in which nodes encode objects and edges relations. At the same time, they can be parsed in O (n² + nm) , where m and n are the sizes of the grammar and the input graph, respectively. In this work, we provide an initial algebra semantic for OPDGs, which allows us to view them as regular tree grammars under an equivalence theory. This makes it possible to transfer results from the field of formal tree languages to the domain of OPDGs, both in the unweighted and the weighted case. In particular, we show that deterministic OPDGs can be minimised efficiently, and that they are learnable under the \minimal adequeate teacher" paradigm, that is, by querying an oracle for equivalence between languages, and membership of individual graphs. To conclude, we demonstrate that the languages generated by OPDGs are definable in monadic second-order logic.