El conocimiento lingüístico en NLP: el puente entre la sintaxis y la semántica

Modern artificial intelligence has made significant strides in natural language processing (NLP), yet the question of whether machines genuinely understand language remains unresolved. Linguistic knowledge, encompassing the rules and meanings humans use for coherent communication, plays a crucial role in this discourse. Traditional NLP relied on structured linguistic theories, but the advent of deep learning has shifted focus to data-driven models that learn from vast datasets.

A new approach utilizing artificial intelligence (AI) is transforming lung cancer staging by enhancing the accuracy and reliability of tumor identification and measurement through advanced image segmentation techniques. This hybrid method combines deep learning with clinical knowledge to provide a more precise assessment of lung tumors, addressing the critical issue of misdiagnosis in cancer treatment.

Google is developing an Android-based operating system named Aluminium, intended to replace ChromeOS on laptops. This initiative was revealed through a job listing for a senior product manager, emphasizing the integration of artificial intelligence, particularly with the Gemini project.

Google has indicated that the Google Assistant will be phased out in Android Auto by March 2026, as the company shifts focus towards its new AI model, Gemini. This transition marks a significant change in how users will interact with AI in their vehicles, moving away from the established Assistant framework.

The Sentence Smith framework has been introduced as a novel approach to controllable text generation in natural language processing (NLP), consisting of parsing sentences into semantic graphs, applying manipulation rules, and generating text from these graphs. This method aims to enhance the transparency and controllability of text generation processes.

AnyExperts has introduced a dynamic routing framework for multimodal language models, allowing for on-demand expert allocation based on the semantic importance of tokens. This approach addresses the inefficiencies of traditional methods that activate a fixed number of experts, leading to better resource utilization and performance in large vision-language systems.

A new dataset named VisReason has been introduced to enhance visual Chain-of-Thought (CoT) reasoning in multimodal large language models (MLLMs). Comprising 489,000 annotated examples across four domains, VisReason aims to facilitate complex reasoning by providing multi-round, human-like rationales that guide MLLMs through visual reasoning steps. Additionally, a subset called VisReason-Pro, featuring 165,000 examples, has been curated with expert-level annotations.

A recent study has revisited the effectiveness of large language models (LLMs) like GPT and Claude in analyzing media frames, particularly in the context of news coverage surrounding the US Mpox epidemic of 2022. The research systematically evaluated these generative models against traditional methods, revealing that manual coders consistently outperformed LLMs in frame analysis tasks.