Parsing sentences into syntax trees can benefit downstream applications inNLP.

Sc The subset of examples inS that belong to classc: Sc = {(xi,yi)|(xi,yi) S,yi =c} f The base model.

Angeliki Stogia has been selected as the Labour Party candidate for the upcoming Gorton and Denton by-election. The Manchester councillor was chosen to represent the party after Greater Manchester mayor Andy Burnham was denied permission to enter the contest a week ago. The by-election on 26 February in the Greater Manchester constituency was prompted by the resignation of former MP Andrew Gwynne on health grounds. Stogia said she was thrilled and excited as a proud Mancunian woman to start campaigning in the constituency. She said she was so looking forward to going out on the doorstep and winning this for Labour.

Natural language understanding inherently depends on integrating multiple complementary perspectives spanning from surface syntax to deep semantics and world knowledge. However, current Aspect-Based Sentiment Analysis (ABSA) systems typically exploit isolated linguistic views, thereby overlooking the intricate interplay between structural representations that humans naturally leverage. We propose CMV-Fuse, a Cross-Modal View fusion framework that emulates human language processing by systematically combining multiple linguistic perspectives. Our approach systematically orchestrates four linguistic perspectives: Abstract Meaning Representations, constituency parsing, dependency syntax, and semantic attention, enhanced with external knowledge integration. Through hierarchical gated attention fusion across local syntactic, intermediate semantic, and global knowledge levels, CMV-Fuse captures both fine-grained structural patterns and broad contextual understanding. A novel structure aware multi-view contrastive learning mechanism ensures consistency across complementary representations while maintaining computational efficiency. Extensive experiments demonstrate substantial improvements over strong baselines on standard benchmarks, with analysis revealing how each linguistic view contributes to more robust sentiment analysis.

Recent advances in natural language processing with large neural models have opened new possibilities for syntactic analysis based on machine learning. This work explores a novel approach to phrase-structure analysis by fine-tuning large language models (LLMs) to translate an input sentence into its corresponding syntactic structure. The main objective is to extend the capabilities of MiSintaxis, a tool designed for teaching Spanish syntax. Several models from the Hugging Face repository were fine-tuned using training data generated from the AnCora-ES corpus, and their performance was evaluated using the F1 score. The results demonstrate high accuracy in phrase-structure analysis and highlight the potential of this methodology.

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.

Parsing sentences into syntax trees can benefit downstream applications in NLP . Transition-based parsers build trees by executing actions in a state transition system.

As anyone with even a trace of a regional dialect who has had to pay a parking fine can attest, voice recognition services struggle with accents. Now, people in Mark Sewards' constituency in Leeds are likely to find the same problem with his AI variant. A chatbot billed as the first AI version of an MP responds in Sewards' voice with advice, support or by offering to pass on a message to his team – but only if it understands you. The website, a virtual representation of the MP for Leeds South West and Morley – complete with a Pixar-style cartoon – was launched by a local startup to field questions from his constituents, some of whom have broad Leeds accents. I was interested to see how "Sewardsbot" would handle a conversation with someone from only a couple of miles away from his constituency border.

As a small number of Tesla robotaxis continue to pick up and drop off a select few Tesla influencers in Austin, Texas, a state legislator who represents part of the electric automakers' limited service area says she's concerned the cars' driving is "less reliable" than the typical human driver. Videos posted online show some "moving violations" that "could be very serious," state senator Sarah Eckhardt, a Democrat who represents Texas' 14th district, told WIRED in an interview. "My constituency is particularly tech savvy and excited about this [autonomous vehicle] technology, but my constituency is also very concerned about public safety, and we can hit the right balance." Last week, as the hours before the debut of Tesla's robotaxi service ticked down, Eckhardt was one of seven Texas Democratic lawmakers who sent a letter to Tesla field quality director Eddie Gates asking the company to delay its plans to launch. Texas has for years had loose rules and oversight around autonomous vehicle operations, making it an attractive place for tech developers to test and launch.

Recent advances in AI research make it increasingly plausible that artificial agents with consequential real-world impact will soon operate beyond tightly controlled environments. Ensuring that these agents are not only safe but that they adhere to broader normative expectations is thus an urgent interdisciplinary challenge. Multiple fields -- notably AI Safety, AI Alignment, and Machine Ethics -- claim to contribute to this task. However, the conceptual boundaries and interrelations among these domains remain vague, leaving researchers without clear guidance in positioning their work. To address this meta-challenge, we develop a structured conceptual framework for understanding AI alignment. Rather than focusing solely on alignment goals, we introduce a taxonomy distinguishing the alignment aim (safety, ethicality, legality, etc.), scope (outcome vs. execution), and constituency (individual vs. collective). This structural approach reveals multiple legitimate alignment configurations, providing a foundation for practical and philosophical integration across domains, and clarifying what it might mean for an agent to be aligned all-things-considered.